A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The im...A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The image obtained from one camera was inversely projected to the plane and then transformed to the view from another camera.The points on the plane shared the same position between original image and the transformed image.As a result,the cast shadows can be detected.In order to improve the efficiency of cast shadow detection and decrease computational complexity,the obvious object areas in CIELAB color space were removed and the potential shadow areas were obtained.Experimental results demonstrate that the proposed approach can detect cast shadows accurately even under various illuminations.展开更多
Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predi...Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks(CTBNs) is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.展开更多
A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search (3DRS) algorithm is used in order to obtain motion information accurately. A motion feature descrip...A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search (3DRS) algorithm is used in order to obtain motion information accurately. A motion feature descriptor (MFD) is designed to describe motion feature of each block in a picture based on motion intensity, motion in occlusion areas, and motion correlation among neighbouring blocks. Then, a fuzzy C-means clustering algorithm (FCM) is implemented based on those MFDs so as to segment moving objects. Moreover, a new parameter named as gathering degree is used to distinguish foreground moving objects and background motion. Experimental results demonstrate the effectiveness of the proposed method.展开更多
The development of spatio-temporal database systems is primarily motivated by applications which track and present mobile objects. In this paper, solutions for establishing the moving object database based on GPS/GIS ...The development of spatio-temporal database systems is primarily motivated by applications which track and present mobile objects. In this paper, solutions for establishing the moving object database based on GPS/GIS environment are presented, and a data modeling of moving object is given by using Temporal logical to extent the query language, finally the application model in cargo delivery system is shown.展开更多
This paper describes an algorithm of collision detection between moving objects in machin-ing process simulation. Graphical simulation of machining has been recognized to be useful for NCprogram verification , since t...This paper describes an algorithm of collision detection between moving objects in machin-ing process simulation. Graphical simulation of machining has been recognized to be useful for NCprogram verification , since the programmer of the machining operator can easily find some faults inthe NC program visually. But it is difficult to visually detect collisions arnong moving objects such ascutting tools , workpieces and fixtures, a data structure to represent moving objects and an algorithmof collision detection between moving objects are proposed. A moving object can be represented by ahierarchical sphere octree and its motion can be described by a quadratic function of time. A collisionoccurs in the case that the distance between any two sphere centers in the respective two moving ob-jects is equal to the sum of the radii of these two spheres, and the radii of these two spheres are lessthan a given precision. By solving the equations that satisfy the conditions of collision between thespheres recursively , we obtain the time and the position of the collision between these two moving ob-Jects.展开更多
Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future ...Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.展开更多
Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents...Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.展开更多
Spatiotemporal data represent the real-world objects that move in geographic space over time.The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data.This ...Spatiotemporal data represent the real-world objects that move in geographic space over time.The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data.This leads to the need for scalable spatiotemporal data management systems.Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory.They shall also provide a range of query processing operators that may scale out in a cloud setting.Currently,very few researches have been conducted to meet this requirement.This paper proposes a Hadoop extension with a spatiotemporal algebra.The algebra consists of moving object types added as Hadoop native types,and operators on top of them.The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data,and for operators that can be unary or binary.Both the types and operators are accessible for the MapReduce jobs.Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis.Certain queries may call more than one operator for different jobs and keep these operators running in parallel.This paper describes the design and implementation of this algebra,and evaluates it using a benchmark that is specific to moving object databases.展开更多
Distance-based range search is crucial in many real applications.In particular,given a database and a query issuer,a distance-based range search retrieves all the objects in the database whose distances from the query...Distance-based range search is crucial in many real applications.In particular,given a database and a query issuer,a distance-based range search retrieves all the objects in the database whose distances from the query issuer are less than or equal to a given threshold.Often,due to the accuracy of positioning devices,updating protocols or characteristics of applications(for example,location privacy protection),data obtained from real world are imprecise or uncertain.Therefore, existing approaches over exact databases cannot be directly applied to the uncertain scenario.In this paper,we redefine the distance-based range query in the context of uncertain databases,namely the probabilistic uncertain distance-based range (PUDR) queries,which obtain objects with confidence guarantees.We categorize the topological relationships between uncertain objects and uncertain search ranges into six cases and present the probability evaluation in each case.It is verified by experiments that our approach outperform Monte-Carlo method utilized in most existing work in precision and time cost for uniform uncertainty distribution.This approach approximates the probabilities of objects following other practical uncertainty distribution,such as Gaussian distribution with acceptable errors.Since the retrieval of a PUDR query requires accessing all the objects in the databases,which is quite costly,we propose spatial pruning and probabilistic pruning techniques to reduce the search space.Two metrics,false positive rate and false negative rate are introduced to measure the qualities of query results.An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.展开更多
To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM...To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM( 1,1 ) model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object's movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects.展开更多
The transmission of video content over a network raises various issues relating to copyright authenticity,ethics,legality,and privacy.The protection of copyrighted video content is a significant issue in the video ind...The transmission of video content over a network raises various issues relating to copyright authenticity,ethics,legality,and privacy.The protection of copyrighted video content is a significant issue in the video industry,and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media.However,there are stillmany unresolved challenges.This paper aims to address those challenges by proposing a new technique for detectingmoving objects in digital videos,which can help prove the credibility of video content by detecting any fake objects inserted by hackers.The proposed technique involves using two methods,the H.264 and the extraction color features methods,to embed and extract watermarks in video frames.The study tested the performance of the system against various attacks and found it to be robust.The evaluation was done using different metrics such as Peak-Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index Measure(SSIM),Bit Correction Ratio(BCR),and Normalized Correlation.The accuracy of identifying moving objects was high,ranging from 96.3%to 98.7%.The system was also able to embed a fragile watermark with a success rate of over 93.65%and had an average capacity of hiding of 78.67.The reconstructed video frames had high quality with a PSNR of at least 65.45 dB and SSIMof over 0.97,making them imperceptible to the human eye.The system also had an acceptable average time difference(T=1.227/s)compared with other state-of-the-art methods.展开更多
In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures (indexes) have been proposed to handle queries of moving points, for example, the well-...In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures (indexes) have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.展开更多
In this paper, the collision problem of two moving objects is investigated. The objects are described by two algebraic sets (ellipses or circles in the paper). The collision problem discussed involves both static an...In this paper, the collision problem of two moving objects is investigated. The objects are described by two algebraic sets (ellipses or circles in the paper). The collision problem discussed involves both static and dynamic case. The static case is that each object moves with known velocity. We use nonlinear programming to decide whether the objects collide. The dynamic case is that each object is controlled by a constraint external force which can be regulated online. For the dynamic case, the collision problem can be modelled as a Minmax problem which can be solved by using differential games. If collision occurs, the time and place of the first collision are given. The moving trajectories are provided in the paper.展开更多
Computer vision systems have an impressive spread both for their practicalapplication and for theoretical research . The common approach used in such systems consists of agood segmentation of moving objects from video...Computer vision systems have an impressive spread both for their practicalapplication and for theoretical research . The common approach used in such systems consists of agood segmentation of moving objects from video sequences . This paper presents an automaticalgorithm for segmenting and extracting moving objects suitable for indoor and outdoor videoapplications, where the background scene can be captured beforehand . Since edge detection is oftenused to extract accurate boundaries of the image's objects, the first step in our algorithm isaccomplished by combining two edge maps which are detected from the frame difference in twoconsecutive frames and the background subtraction . After removing edge points that belong to thebackground, the resulting moving edge map is fed to the object extraction step . A fundamental taskin this step is to declare the candidates of the moving object, followed by applying morphologicaloperations. The algorithm is implemented on a real video sequence as well as MPEG- 4 sequence andgood segmentation results are achieved.展开更多
This paper proposes a novel method, primarily based on the fuzzy adaptive resonance theory (ART) neural network with forgetting procedure, for moving object detection and background modeling in natural scenes. With ...This paper proposes a novel method, primarily based on the fuzzy adaptive resonance theory (ART) neural network with forgetting procedure, for moving object detection and background modeling in natural scenes. With the ability, inheriting from the ART neural network, of extracting patterns from arbitrary sequences, the background model based on the proposed method can learn new scenes quickly and accurately. To guarantee that a long-life model can derived from the proposed mothed, a forgetting procedure is employed to find the neuron that needs to be discarded and reconstructed, and the finding procedure is based on a neural network which can find the extreme value quickly. The results of a suite of quantitative and qualitative experiments conducted verify that for processes of modeling background and detecting moving objects our method is more effective than five other proven methods with which it is compared.展开更多
Data obtained from real world are imprecise or uncertain due to the accuracy of positioning devices,updating protocols or characteristics of applications.On the other hand,users sometimes prefer to qualitatively expre...Data obtained from real world are imprecise or uncertain due to the accuracy of positioning devices,updating protocols or characteristics of applications.On the other hand,users sometimes prefer to qualitatively express their requests with vague conditions and different parts of search region are in-equally important in some applications.We address the problem of efficiently processing the fuzzy range queries for uncertain moving objects whose whereabouts in time are not known exactly,for which the basic syntax is find objects always/sometimes near to the query issuer with the qualifying guarantees no less than a given threshold during a given temporal interval.We model the location uncertainty of moving objects on the utilization of probability density functions and describe the indeterminate boundary of query range with fuzzy set.We present the qualifying guarantee evaluation of objects,and propose pruning techniques based on the α-cut of fuzzy set to shrink the search space efficiently.We also design rules to reject non-qualifying objects and validate qualifying objects in order to avoid unnecessary costly numeric integrations in the refinement step.An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of algorithms under various experimental展开更多
Augmented virtual environments(AVE)combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously.AVE can provide solutions for continuous...Augmented virtual environments(AVE)combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously.AVE can provide solutions for continuous tracking of moving objects,camera scheduling,and path planning in the real world.This paper proposes a novel approach for 3D path prediction of moving objects in a video-augmented indoor virtual environment.The study includes 3D motion analysis of moving objects,multi-path prediction,hierarchical visualization,and path-based multi-camera scheduling.The results show that these methods can give a closed-loop process of 3D path prediction and continuous tracking of moving objects in an AVE.The path analysis algorithms proved accurate and time-efficient,costing less than 1.3 ms to get the optimal path.The experiment ran a 3D scene containing 295,000 triangles at around 35 frames per second on a laptop with 1 GB of graphics card memory,which means the performance of the proposed methods is good enough to maintain high rendering efficiency for a video-augmented indoor virtual scene.展开更多
Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over ...Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS (branch-and-bound skyline with rescanning and repacking) and TPBBS (time-parameterized branch- and-bound skyline), each with two alternative methods, to handle predictive skyline computation. The basic TPRBS is further extended to TPBBSE (TPBBS with expansion) to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.展开更多
A novel and effective approach to global motion estimation and moving object extraction is proposed. First, the translational motion model is used because of the fact that complex motion can be decomposed as a sum of ...A novel and effective approach to global motion estimation and moving object extraction is proposed. First, the translational motion model is used because of the fact that complex motion can be decomposed as a sum of translational components. Then in this application, the edge gray horizontal and vertical projections are used as the block matching feature for the motion vectors estimation. The proposed algorithm reduces the motion estimation computations by calculating the onedimensional vectors rather than the two-dimensional ones. Once the global motion is robustly estimated, relatively stationary background can be almost completely eliminated through the inter-frame difference method. To achieve an accurate object extraction result, the higher-order statistics (HOS) algorithm is used to discriminate backgrounds and moving objects. Experimental results validate that the proposed method is an effective way for global motion estimation and object extraction.展开更多
An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change (MIC) algor...An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change (MIC) algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus (RANSAC) method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.展开更多
基金Project(40971219)supported by the Natural Science Foundation of ChinaProjects(201121202020005,T201221207)supported by the Fundamental Research Fund for the Central Universities,China
文摘A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The image obtained from one camera was inversely projected to the plane and then transformed to the view from another camera.The points on the plane shared the same position between original image and the transformed image.As a result,the cast shadows can be detected.In order to improve the efficiency of cast shadow detection and decrease computational complexity,the obvious object areas in CIELAB color space were removed and the potential shadow areas were obtained.Experimental results demonstrate that the proposed approach can detect cast shadows accurately even under various illuminations.
基金supported by the National Natural Science Foundation of China (Nos.61100045,61165013,61003142,60902023,and 61171096)the China Postdoctoral Science Foundation (Nos.20090461346,201104697)+3 种基金the Youth Foundation for Humanities and Social Sciences of Ministry of Education of China (No.10YJCZH117)the Fundamental Research Funds for the Central Universities (Nos.SWJTU09CX035,SWJTU11ZT08)Zhejiang Provincial Natural Science Foundation of China (Nos.Y1100589,Y1080123)the Natural Science Foundation of Ningbo,China (No.2011A610175)
文摘Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks(CTBNs) is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.
基金Supported by the National Natural Science Foundation of China (No. 60772134, 60902081, 60902052) the 111 Project (No.B08038) the Fundamental Research Funds for the Central Universities(No.72105457).
文摘A novel moving objects segmentation method is proposed in this paper. A modified three dimensional recursive search (3DRS) algorithm is used in order to obtain motion information accurately. A motion feature descriptor (MFD) is designed to describe motion feature of each block in a picture based on motion intensity, motion in occlusion areas, and motion correlation among neighbouring blocks. Then, a fuzzy C-means clustering algorithm (FCM) is implemented based on those MFDs so as to segment moving objects. Moreover, a new parameter named as gathering degree is used to distinguish foreground moving objects and background motion. Experimental results demonstrate the effectiveness of the proposed method.
基金Supported by the National Science Research Project (No.2001BA205A18)
文摘The development of spatio-temporal database systems is primarily motivated by applications which track and present mobile objects. In this paper, solutions for establishing the moving object database based on GPS/GIS environment are presented, and a data modeling of moving object is given by using Temporal logical to extent the query language, finally the application model in cargo delivery system is shown.
文摘This paper describes an algorithm of collision detection between moving objects in machin-ing process simulation. Graphical simulation of machining has been recognized to be useful for NCprogram verification , since the programmer of the machining operator can easily find some faults inthe NC program visually. But it is difficult to visually detect collisions arnong moving objects such ascutting tools , workpieces and fixtures, a data structure to represent moving objects and an algorithmof collision detection between moving objects are proposed. A moving object can be represented by ahierarchical sphere octree and its motion can be described by a quadratic function of time. A collisionoccurs in the case that the distance between any two sphere centers in the respective two moving ob-jects is equal to the sum of the radii of these two spheres, and the radii of these two spheres are lessthan a given precision. By solving the equations that satisfy the conditions of collision between thespheres recursively , we obtain the time and the position of the collision between these two moving ob-Jects.
基金Partly supported by the National Natural Science Foundation of China (Grant No. 60573091), the Key Project of Ministry of Education of China (Grant No. 03044), Program for New Century Excellent Talents in University (NCET), Program for Creative Ph.D. Thesis in University. Acknowledgments The authors would like to thank Hai-Xun Wang from IBM T. J. Watson Research, Karine Zeitouni from PRISM, Versailles Saint- Quentin University in France and Stephane Grumbach from CNRS, LIAMA China for many helpful advices.
文摘Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.
基金Project (No. 60805001) partially supported by the National NaturalScience Foundation of China
文摘Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.
文摘Spatiotemporal data represent the real-world objects that move in geographic space over time.The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data.This leads to the need for scalable spatiotemporal data management systems.Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory.They shall also provide a range of query processing operators that may scale out in a cloud setting.Currently,very few researches have been conducted to meet this requirement.This paper proposes a Hadoop extension with a spatiotemporal algebra.The algebra consists of moving object types added as Hadoop native types,and operators on top of them.The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data,and for operators that can be unary or binary.Both the types and operators are accessible for the MapReduce jobs.Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis.Certain queries may call more than one operator for different jobs and keep these operators running in parallel.This paper describes the design and implementation of this algebra,and evaluates it using a benchmark that is specific to moving object databases.
基金supported by the National High Technology Research and Development 863 Program of China under Grant No. 2007AA01Z404the Program of Jiangsu Province under Grant No.BE2008135.
文摘Distance-based range search is crucial in many real applications.In particular,given a database and a query issuer,a distance-based range search retrieves all the objects in the database whose distances from the query issuer are less than or equal to a given threshold.Often,due to the accuracy of positioning devices,updating protocols or characteristics of applications(for example,location privacy protection),data obtained from real world are imprecise or uncertain.Therefore, existing approaches over exact databases cannot be directly applied to the uncertain scenario.In this paper,we redefine the distance-based range query in the context of uncertain databases,namely the probabilistic uncertain distance-based range (PUDR) queries,which obtain objects with confidence guarantees.We categorize the topological relationships between uncertain objects and uncertain search ranges into six cases and present the probability evaluation in each case.It is verified by experiments that our approach outperform Monte-Carlo method utilized in most existing work in precision and time cost for uniform uncertainty distribution.This approach approximates the probabilities of objects following other practical uncertainty distribution,such as Gaussian distribution with acceptable errors.Since the retrieval of a PUDR query requires accessing all the objects in the databases,which is quite costly,we propose spatial pruning and probabilistic pruning techniques to reduce the search space.Two metrics,false positive rate and false negative rate are introduced to measure the qualities of query results.An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.
文摘To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM( 1,1 ) model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object's movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects.
文摘The transmission of video content over a network raises various issues relating to copyright authenticity,ethics,legality,and privacy.The protection of copyrighted video content is a significant issue in the video industry,and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media.However,there are stillmany unresolved challenges.This paper aims to address those challenges by proposing a new technique for detectingmoving objects in digital videos,which can help prove the credibility of video content by detecting any fake objects inserted by hackers.The proposed technique involves using two methods,the H.264 and the extraction color features methods,to embed and extract watermarks in video frames.The study tested the performance of the system against various attacks and found it to be robust.The evaluation was done using different metrics such as Peak-Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index Measure(SSIM),Bit Correction Ratio(BCR),and Normalized Correlation.The accuracy of identifying moving objects was high,ranging from 96.3%to 98.7%.The system was also able to embed a fragile watermark with a success rate of over 93.65%and had an average capacity of hiding of 78.67.The reconstructed video frames had high quality with a PSNR of at least 65.45 dB and SSIMof over 0.97,making them imperceptible to the human eye.The system also had an acceptable average time difference(T=1.227/s)compared with other state-of-the-art methods.
基金supported in part by Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT0652)the National Natural Science Foundation of China (Grant No. 60603044).
文摘In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures (indexes) have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.
文摘In this paper, the collision problem of two moving objects is investigated. The objects are described by two algebraic sets (ellipses or circles in the paper). The collision problem discussed involves both static and dynamic case. The static case is that each object moves with known velocity. We use nonlinear programming to decide whether the objects collide. The dynamic case is that each object is controlled by a constraint external force which can be regulated online. For the dynamic case, the collision problem can be modelled as a Minmax problem which can be solved by using differential games. If collision occurs, the time and place of the first collision are given. The moving trajectories are provided in the paper.
文摘Computer vision systems have an impressive spread both for their practicalapplication and for theoretical research . The common approach used in such systems consists of agood segmentation of moving objects from video sequences . This paper presents an automaticalgorithm for segmenting and extracting moving objects suitable for indoor and outdoor videoapplications, where the background scene can be captured beforehand . Since edge detection is oftenused to extract accurate boundaries of the image's objects, the first step in our algorithm isaccomplished by combining two edge maps which are detected from the frame difference in twoconsecutive frames and the background subtraction . After removing edge points that belong to thebackground, the resulting moving edge map is fed to the object extraction step . A fundamental taskin this step is to declare the candidates of the moving object, followed by applying morphologicaloperations. The algorithm is implemented on a real video sequence as well as MPEG- 4 sequence andgood segmentation results are achieved.
文摘This paper proposes a novel method, primarily based on the fuzzy adaptive resonance theory (ART) neural network with forgetting procedure, for moving object detection and background modeling in natural scenes. With the ability, inheriting from the ART neural network, of extracting patterns from arbitrary sequences, the background model based on the proposed method can learn new scenes quickly and accurately. To guarantee that a long-life model can derived from the proposed mothed, a forgetting procedure is employed to find the neuron that needs to be discarded and reconstructed, and the finding procedure is based on a neural network which can find the extreme value quickly. The results of a suite of quantitative and qualitative experiments conducted verify that for processes of modeling background and detecting moving objects our method is more effective than five other proven methods with which it is compared.
基金supported by the National High Technology Research and Development 863 Program of China under Grant No. 2007AA01Z404the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20103218110017+1 种基金the Science & Technology Pillar Program of Jiangsu Province of China under Grant No. BE2008135the Postdoctoral Science Foundation of China under Grant No. 20100481133.
文摘Data obtained from real world are imprecise or uncertain due to the accuracy of positioning devices,updating protocols or characteristics of applications.On the other hand,users sometimes prefer to qualitatively express their requests with vague conditions and different parts of search region are in-equally important in some applications.We address the problem of efficiently processing the fuzzy range queries for uncertain moving objects whose whereabouts in time are not known exactly,for which the basic syntax is find objects always/sometimes near to the query issuer with the qualifying guarantees no less than a given threshold during a given temporal interval.We model the location uncertainty of moving objects on the utilization of probability density functions and describe the indeterminate boundary of query range with fuzzy set.We present the qualifying guarantee evaluation of objects,and propose pruning techniques based on the α-cut of fuzzy set to shrink the search space efficiently.We also design rules to reject non-qualifying objects and validate qualifying objects in order to avoid unnecessary costly numeric integrations in the refinement step.An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of algorithms under various experimental
基金supported by the National Natural Science Foundation of China[grant number 41901328 and 41974108]the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA19080101]the National Key Research and Development Program of China[grant number 2016YFB0501503 and 2016YFB0501502].
文摘Augmented virtual environments(AVE)combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously.AVE can provide solutions for continuous tracking of moving objects,camera scheduling,and path planning in the real world.This paper proposes a novel approach for 3D path prediction of moving objects in a video-augmented indoor virtual environment.The study includes 3D motion analysis of moving objects,multi-path prediction,hierarchical visualization,and path-based multi-camera scheduling.The results show that these methods can give a closed-loop process of 3D path prediction and continuous tracking of moving objects in an AVE.The path analysis algorithms proved accurate and time-efficient,costing less than 1.3 ms to get the optimal path.The experiment ran a 3D scene containing 295,000 triangles at around 35 frames per second on a laptop with 1 GB of graphics card memory,which means the performance of the proposed methods is good enough to maintain high rendering efficiency for a video-augmented indoor virtual scene.
基金supported by the National Natural Science Foundation of China (Nos. 60603044 and 60803003)the Program for Changjiang Scholars and Innovative Research Team in University(No. IRT0652)
文摘Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS (branch-and-bound skyline with rescanning and repacking) and TPBBS (time-parameterized branch- and-bound skyline), each with two alternative methods, to handle predictive skyline computation. The basic TPRBS is further extended to TPBBSE (TPBBS with expansion) to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.
基金The National Natural Science Foundation of China(No.60574006)
文摘A novel and effective approach to global motion estimation and moving object extraction is proposed. First, the translational motion model is used because of the fact that complex motion can be decomposed as a sum of translational components. Then in this application, the edge gray horizontal and vertical projections are used as the block matching feature for the motion vectors estimation. The proposed algorithm reduces the motion estimation computations by calculating the onedimensional vectors rather than the two-dimensional ones. Once the global motion is robustly estimated, relatively stationary background can be almost completely eliminated through the inter-frame difference method. To achieve an accurate object extraction result, the higher-order statistics (HOS) algorithm is used to discriminate backgrounds and moving objects. Experimental results validate that the proposed method is an effective way for global motion estimation and object extraction.
文摘An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change (MIC) algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus (RANSAC) method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.