Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd st...Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.展开更多
In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the genera-lized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segment...In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the genera-lized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segmentation is solved under an algebra framework. Our main contribution is to propose a post-processing procedure, which can detect the boundaries of motion layers and further determine the layer ordering. Test results on real imagery have confirmed the validity of our method.展开更多
Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from ...Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera. Some existing algorithms cannot adapt to changing circumstances and require manual calibration in terms of specification of parameters or some hypotheses for changing background. An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient. RGB color space is selected instead of introducing complex color models to segment moving objects and suppress shadows. A color ratio for 4-connected neighbors of a pixel and multi-scale wavelet transformation are combined to suppress shadows. The mentioned approach is scene-independent and high correct segmentation. It has been shown that the approach is robust and efficient to detect moving objects by experiments.展开更多
Background：The development of mechanically active culture systems helps increase the understanding of the role of mechanical stress in intervertebral disc （IVD） degeneration.Motion segment cultures allow for preser...Background：The development of mechanically active culture systems helps increase the understanding of the role of mechanical stress in intervertebral disc （IVD） degeneration.Motion segment cultures allow for preservation of the native IVD structure,and adjacent vertebral bodies facilitate the application and control of mechanical loads.The purpose of this study was to establish loading and organ culture methods for rabbit IVD motion segments to study the effect of static load on the whole disc organ.Methods：IVD motion segments were harvested from rabbit lumbar spines and cultured in no-loading 6-well plates （control conditions） or custom-made apparatuses under a constant,compressive load （3 kg,0.5 MPa） for up to 14 days.Tissue integrity,matrix synthesis,and the matrix gene expression profile were assessed after 3,7,and 14 days of culturing and compared with those of fresh tissues.Results：The results showed that ex vivo culturing of motion segments preserved tissue integrity under no-loading conditions for 14 days whereas the static load gradually destroyed the morphology after 3 days.Proteoglycan contents were decreased under both conditions,with a more obvious decrease under static load,and proteoglycan gene expression was also downregulated.However,under static load,immunohistochemical staining intensity and collagen Type Ⅱ alpha 1 （COL2A 1） gene expression were significantly enhanced （61.54 ± 5.91,P =0.035） and upregulated （1.195 ± 0.040,P =0.000）,respectively,compared with those in the controls （P 〈 0.05）.In contrast,under constant compression,these trends were reversed.Our initial results indicated that short-term static load stimulated the synthesis of collagen Type Ⅱ alpha l;however,sustained constant compression led to progressive degeneration and specifically to a decreased proteoglycan content.Conclusions：A loading and organ culture system for ex vivo rabbit IVD motion segments was developed.Using this system,we were able to study the effects of mechanical stimulation on the biology of IVDs,as well as the pathomechanics of IVD degeneration.展开更多
Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However...Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition （HHD） based object-motion oriented map （OOM）. By decomposing the image motion （optical flow） into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%-20% even over challenging scenes with complex background.展开更多
Motion segmentation plays an important role in many vision applications,yet it is still a challenging problem in complex scenes.The typical conditions in real world scenarios like illumination variations,dynamic backg...Motion segmentation plays an important role in many vision applications,yet it is still a challenging problem in complex scenes.The typical conditions in real world scenarios like illumination variations,dynamic backgrounds and camera shaking make negative effects on segmentation performance.In this paper,a newly designed method for robust motion segmentation is proposed,which is mainly composed of two interrelated models.One is a normal random model（N-model）,and the other is called enhanced random model（E-model）.They are constructed and updated in spatio-temporal information for adapting to illumination changes and dynamic backgrounds,and operate in an AdaBoost-like strategy.The exhaustive experimental evaluations on complex scenes demonstrate that the proposed method outperforms the state-of-the-art methods.展开更多
Objective： To observe the tested results of the segmental range of motion （ROM） of lumbar spine by charge couple device （CCD）-based system for 3-dimensional real-time positioning （CCD system）, and to analyze it...Objective： To observe the tested results of the segmental range of motion （ROM） of lumbar spine by charge couple device （CCD）-based system for 3-dimensional real-time positioning （CCD system）, and to analyze its clinical significance. Methods： Seven patients with lumbar joint dysfunction and 8 healthy subjects were tested twice by the CCD-based system with an interval of 10 min. Results： The ROM of the patients was obviously lesser than that of the healthy subjects. The measuring data of segmental ROM of lumbar spine by CCD system is correlated significantly to the same data checked later on the same subjects in every direction of the movements. The differences between two checks are usually less than 1 degree. Conclusion： Specially designed CCD based system for 3-dimensional real-time positioning could objectively reflect the segmental ROM of lumbar spine. The system would be of great clinical significance in the assessment of the biomechanical dysfunction of lumbar spine and the effect of the treatment applied.展开更多
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 segmentati...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.展开更多
This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching ...This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pro- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.展开更多
As one of the most important daily motor activities, human locomotion has been investigated intensively in recent decades. The locomotor functions and mechanics of human lower limbs have become relatively well underst...As one of the most important daily motor activities, human locomotion has been investigated intensively in recent decades. The locomotor functions and mechanics of human lower limbs have become relatively well understood. However, so far our understanding of the motions and functional contributions of the human spine during locomotion is still very poor and simultaneous in-vivo limb and spinal column motion data are scarce. The objective of this study is to investigate the delicate in-vivo kinematic coupling between different functional regions of the human spinal column during locomotion as a stepping stone to explore the locomotor function of the human spine complex. A novel infrared reflective marker cluster system was constrncted using stereophotogrammetry techniques to record the 3D in-vivo geometric shape of the spinal column and the segmental position and orientation of each functional spinal region simultaneously. Gait measurements of normal walking were conducted. The preliminary results show that the spinal column shape changes periodically in the frontal plane during locomotion. The segmental motions of different spinal functional regions appear to be strongly coupled, indicating some synergistic strategy may be employed by the human spinal column to facilitate locomotion. In contrast to traditional medical imaging-based methods, the proposed technique can be used to investigate the dynamic characteristics of the spinal column, hence providing more insight into the functional biomechanics of the human spine.展开更多
In previous papers, the author considered the model of anomalous diffusion, defined by stable random process on an interval with reflecting edges. Estimates of the rate convergence of this process distribution to a un...In previous papers, the author considered the model of anomalous diffusion, defined by stable random process on an interval with reflecting edges. Estimates of the rate convergence of this process distribution to a uniform distribution are constructed. However, recent physical studies require consideration of models of diffusion, defined not only by stable random process with independent increments but multivariate fractional Brownian motion with dependent increments. This task requires the development of special mathematical techniques evaluation of the rate of convergence of the distribution of multivariate Brownian motion in a segment with reflecting boundaries to the limit. In the present work, this technology is developed and a power estimate of the rate of convergence to the limiting uniform distribution is built.展开更多
Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance...Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.展开更多
基金This research work is supported by the Deputyship of Research&Innovation,Ministry of Education in Saudi Arabia(Grant Number 758).
文摘Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.
文摘In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the genera-lized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segmentation is solved under an algebra framework. Our main contribution is to propose a post-processing procedure, which can detect the boundaries of motion layers and further determine the layer ordering. Test results on real imagery have confirmed the validity of our method.
文摘Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera. Some existing algorithms cannot adapt to changing circumstances and require manual calibration in terms of specification of parameters or some hypotheses for changing background. An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient. RGB color space is selected instead of introducing complex color models to segment moving objects and suppress shadows. A color ratio for 4-connected neighbors of a pixel and multi-scale wavelet transformation are combined to suppress shadows. The mentioned approach is scene-independent and high correct segmentation. It has been shown that the approach is robust and efficient to detect moving objects by experiments.
文摘Background：The development of mechanically active culture systems helps increase the understanding of the role of mechanical stress in intervertebral disc （IVD） degeneration.Motion segment cultures allow for preservation of the native IVD structure,and adjacent vertebral bodies facilitate the application and control of mechanical loads.The purpose of this study was to establish loading and organ culture methods for rabbit IVD motion segments to study the effect of static load on the whole disc organ.Methods：IVD motion segments were harvested from rabbit lumbar spines and cultured in no-loading 6-well plates （control conditions） or custom-made apparatuses under a constant,compressive load （3 kg,0.5 MPa） for up to 14 days.Tissue integrity,matrix synthesis,and the matrix gene expression profile were assessed after 3,7,and 14 days of culturing and compared with those of fresh tissues.Results：The results showed that ex vivo culturing of motion segments preserved tissue integrity under no-loading conditions for 14 days whereas the static load gradually destroyed the morphology after 3 days.Proteoglycan contents were decreased under both conditions,with a more obvious decrease under static load,and proteoglycan gene expression was also downregulated.However,under static load,immunohistochemical staining intensity and collagen Type Ⅱ alpha 1 （COL2A 1） gene expression were significantly enhanced （61.54 ± 5.91,P =0.035） and upregulated （1.195 ± 0.040,P =0.000）,respectively,compared with those in the controls （P 〈 0.05）.In contrast,under constant compression,these trends were reversed.Our initial results indicated that short-term static load stimulated the synthesis of collagen Type Ⅱ alpha l;however,sustained constant compression led to progressive degeneration and specifically to a decreased proteoglycan content.Conclusions：A loading and organ culture system for ex vivo rabbit IVD motion segments was developed.Using this system,we were able to study the effects of mechanical stimulation on the biology of IVDs,as well as the pathomechanics of IVD degeneration.
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61503277.
文摘Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition （HHD） based object-motion oriented map （OOM）. By decomposing the image motion （optical flow） into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%-20% even over challenging scenes with complex background.
基金Supported by the National Natural Science Foundation of China(61502364)Key Scientific and Technological Project of Henan Province(132102210246)+1 种基金Enterprises-Universities-Research Institutes Cooperation Project of Henan Province(142107000022)CERNET Innovation Project(NGII20150311)
文摘Motion segmentation plays an important role in many vision applications,yet it is still a challenging problem in complex scenes.The typical conditions in real world scenarios like illumination variations,dynamic backgrounds and camera shaking make negative effects on segmentation performance.In this paper,a newly designed method for robust motion segmentation is proposed,which is mainly composed of two interrelated models.One is a normal random model（N-model）,and the other is called enhanced random model（E-model）.They are constructed and updated in spatio-temporal information for adapting to illumination changes and dynamic backgrounds,and operate in an AdaBoost-like strategy.The exhaustive experimental evaluations on complex scenes demonstrate that the proposed method outperforms the state-of-the-art methods.
文摘Objective： To observe the tested results of the segmental range of motion （ROM） of lumbar spine by charge couple device （CCD）-based system for 3-dimensional real-time positioning （CCD system）, and to analyze its clinical significance. Methods： Seven patients with lumbar joint dysfunction and 8 healthy subjects were tested twice by the CCD-based system with an interval of 10 min. Results： The ROM of the patients was obviously lesser than that of the healthy subjects. The measuring data of segmental ROM of lumbar spine by CCD system is correlated significantly to the same data checked later on the same subjects in every direction of the movements. The differences between two checks are usually less than 1 degree. Conclusion： Specially designed CCD based system for 3-dimensional real-time positioning could objectively reflect the segmental ROM of lumbar spine. The system would be of great clinical significance in the assessment of the biomechanical dysfunction of lumbar spine and the effect of the treatment applied.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572127）, the Development Foundation of Shanghai Municipal Commission of Education （Grant No.05AZ43）, and the Shanghai Leading Academic Discipline Project （Grant No.T0102）
文摘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.
文摘This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pro- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.
基金supported by the Key Project of National Natural Science Foundation of China (No. 50635030)the National Basic Research Program ("973" Program) of China (No. 2007CB616913)+2 种基金was also supported by the China Scholarship Council (CSC)We also would like to thank Karin Jespers and Sharon Warner of the Structure and Motion Laboratory for their support of the experimental workJRH’s con-tributions were supported by research grants BB/C516844/1 and BB/F01169/1 from the BBSRC, whom we thank.
文摘As one of the most important daily motor activities, human locomotion has been investigated intensively in recent decades. The locomotor functions and mechanics of human lower limbs have become relatively well understood. However, so far our understanding of the motions and functional contributions of the human spine during locomotion is still very poor and simultaneous in-vivo limb and spinal column motion data are scarce. The objective of this study is to investigate the delicate in-vivo kinematic coupling between different functional regions of the human spinal column during locomotion as a stepping stone to explore the locomotor function of the human spine complex. A novel infrared reflective marker cluster system was constrncted using stereophotogrammetry techniques to record the 3D in-vivo geometric shape of the spinal column and the segmental position and orientation of each functional spinal region simultaneously. Gait measurements of normal walking were conducted. The preliminary results show that the spinal column shape changes periodically in the frontal plane during locomotion. The segmental motions of different spinal functional regions appear to be strongly coupled, indicating some synergistic strategy may be employed by the human spinal column to facilitate locomotion. In contrast to traditional medical imaging-based methods, the proposed technique can be used to investigate the dynamic characteristics of the spinal column, hence providing more insight into the functional biomechanics of the human spine.
文摘In previous papers, the author considered the model of anomalous diffusion, defined by stable random process on an interval with reflecting edges. Estimates of the rate convergence of this process distribution to a uniform distribution are constructed. However, recent physical studies require consideration of models of diffusion, defined not only by stable random process with independent increments but multivariate fractional Brownian motion with dependent increments. This task requires the development of special mathematical techniques evaluation of the rate of convergence of the distribution of multivariate Brownian motion in a segment with reflecting boundaries to the limit. In the present work, this technology is developed and a power estimate of the rate of convergence to the limiting uniform distribution is built.
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）.
基金supported by the National Natural Science Foundation of China (No.61872189)。
文摘Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.
