Key techniques for predicting the uncertain trajectories of moving objects with dynamic environment awareness

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.

The Application Model of Moving Objects in Cargo Delivery System

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.

COLLISION DETECTION AMONG MOVING OBJECTSIN MACHINING PROCESS SIMULATION

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.

Indexing Future Trajectories of Moving Objects in a Constrained Network

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.

A spatiotemporal algebra in Hadoop for moving objects

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.

Uncertain Distance-Based Range Queries over Uncertain Moving Objects

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.

New Fragile Watermarking Technique to Identify Inserted Video Objects Using H.264 and Color Features

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.

Adaptive Indexing of Moving Objects with Highly Variable Update Frequencies

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.

Automatic Segmentation of Moving Objects in Video Sequences for Indoor and Outdoor Applications

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.

Neural network based method for background modeling and detecting moving objects

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.

3D Path prediction of moving objects in a video-augmented indoor virtual environment

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.

Fuzzy Distance-Based Range Queries over Uncertain Moving Objects

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

Applied Techniques in Tracking Moving Targets in Marine Using Image Processing

Moving objects tracking is one of the most used categories in the realm of machine vision that has attracted attention of so many researchers in recent decades. Video tracking has various applications in military industries, protective systems and machine vision. Target tracking algorithms vary according to their usages. In this paper, it has been attempted to discuss and analyze mobile target tracking techniques and algorithms in Marine.

Real-time moving object detection for video monitoring systems

Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.

Redundant discrete wavelet transforms based moving object recognition and tracking

A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.

An efficient compressed domain moving object segmentation algorithm based on motion vector field

In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.

Moving object detection in framework of compressive sampling

Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for signals.In the image application with limited resources the camera data can be stored and processed in compressed form.An algorithm for moving object and region detection in video using a compressive sampling is developed.The algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background scene.The algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated background.This leads to a computationally efficient method and a system compared with the existing motion estimation methods.The experimental results show that the sampling rate can reduce to 25% without sacrificing performance.

An Improved Moving Object Detection Algorithm Based on Gaussian Mixture Models

Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving object detection based on Gaussian mixture model and three-frame difference method. In the process of extracting the moving region, the improved three-frame difference method uses the dynamic segmentation threshold and edge detection technology, and it is first used to solve the problems such as the illumination mutation and the discontinuity of the target edge. Then, a new adaptive selection strategy of the number of Gaussian distributions is introduced to reduce the processing time and improve accuracy of detection. Finally, HSV color space is used to remove shadow regions, and the whole moving object is detected. Experimental results show that the proposed algorithm can detect moving objects in various situations effectively.

Imaging a periodic moving/state-changed object with Hadamard-based computational ghost imaging

We propose a method for imaging a periodic moving/state-changed object based on computational ghost imaging with Hadamard speckle patterns and a slow bucket detector, named as PO-HCGI. In the scheme, speckle patterns are produced from a part of each row of a Hadamard matrix. Then, in each cycle, multiple speckle patterns are projected onto the periodic moving/state-changed object, and a bucket detector with a slow sampling rate records the total intensities reflected from the object as one measurement. With a series of measurements, the frames of the moving/state-changed object can be obtained directly by the second-order correlation function based on the Hadamard matrix and the corresponding bucket detector measurement results. The experimental and simulation results demonstrate the validity of the PO-HCGI. To the best of our knowledge, PO-HCGI is the first scheme that can image a fast periodic moving/state-changed object by computational ghost imaging with a slow bucket detector.

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.