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Object Tracking: A Survey
, 2006
"... The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns o ..."
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Cited by 701 (7 self)
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The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Tracking multiple humans in complex situations
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are ..."
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Cited by 134 (3 self)
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Abstract—Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences. Index Terms—Multiple-human segmentation, multiple-human tracking, visual surveillance, human shape model, human locomotion model. 1
Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... In this paper, we address the issue of tracking moving objects in an environment covered by multiple uncalibrated cameras with overlapping fields of view, typical of most surveillance setups. In such a scenario, it is essential to establish correspondence between tracks of the same object, seen in d ..."
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Cited by 124 (3 self)
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In this paper, we address the issue of tracking moving objects in an environment covered by multiple uncalibrated cameras with overlapping fields of view, typical of most surveillance setups. In such a scenario, it is essential to establish correspondence between tracks of the same object, seen in di#erent cameras, to recover complete information about the object. We call this the problem of consistent labeling of objects when seen in multiple cameras. We employ a novel approach of finding the limits of field of view (FOV) of each camera as visible in the other cameras. We show that if the FOV lines are known, it is possible to disambiguate between multiple possibilities for correspondence. We present a method to automatically recover these lines by observing motion in the environment. Furthermore, once these lines are initialized, the homography between the views can also be recovered. We present results on indoor and outdoor sequences, containing persons and vehicles.
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
, 2005
"... Extracting layers from video is very important for video representation, analysis, compression, and synthesis. Assuming that a scene can be approximately described by multiple planar regions, this paper describes a robust and novel approach to automatically extract a set of affine or projective tra ..."
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Cited by 98 (9 self)
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Extracting layers from video is very important for video representation, analysis, compression, and synthesis. Assuming that a scene can be approximately described by multiple planar regions, this paper describes a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, detect the occlusion pixels over multiple consecutive frames, and segment the scene into several motion layers. First, after determining a number of seed regions using correspondences in two frames, we expand the seed regions and reject the outliers employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, an occlusion order constraint on multiple frames is explored, which enforces that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then, the correct layer segmentation is obtained by using a graph cuts algorithm and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust.
Fast multiple object tracking via a hierarchical particle filter
- In The IEEE International Conference on Computer Vision (ICCV
, 2005
"... A very efficient and robust visual object tracking algo-rithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge ori-entation histogram features. While the use of more features and samples can improve the robustness, the computational load re ..."
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Cited by 92 (4 self)
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A very efficient and robust visual object tracking algo-rithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge ori-entation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To acceler-ate the algorithm while retaining robustness we adopt sev-eral enhancements in the algorithm. The first is the use of integral images [34] for efficiently computing the color fea-tures and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robust-ness against clutter or short period occlusions. Experimen-tal results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking. 1
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
"... Abstract—Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multiview approach to solve this problem. In our approach, we neither detect nor track objects from any singl ..."
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Cited by 54 (0 self)
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Abstract—Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multiview approach to solve this problem. In our approach, we neither detect nor track objects from any single camera or camera pair; rather, evidence is gathered from all of the cameras into a synergistic framework and detection and tracking results are propagated back to each view. Unlike other multiview approaches that require fully calibrated views, our approach is purely image-based and uses only 2D constructs. To this end, we develop a planar homographic occupancy constraint that fuses foreground likelihood information from multiple views to resolve occlusions and localize people on a reference scene plane. For greater robustness, this process is extended to multiple planes parallel to the reference plane in the framework of plane to plane homologies. Our fusion methodology also models scene clutter using the Schmieder and Weathersby clutter measure, which acts as a confidence prior, to assign higher fusion weight to views with lesser clutter. Detection and tracking are performed simultaneously by graph cuts segmentation of tracks in the space-time occupancy likelihood data. Experimental results with detailed qualitative and quantitative analysis are demonstrated in challenging multiview crowded scenes. Index Terms—Tracking, sensor fusion, graph-theoretic methods. Ç 1
Segmentation and Tracking of Multiple Humans in Crowded Environments
"... Abstract—Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations by multiple partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood fo ..."
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Cited by 53 (0 self)
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Abstract—Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations by multiple partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood for multiple humans based on the appearance of the humans, the visibility of the body obtained by occlusion reasoning, and foreground/background separation. The optimal solution is obtained by using an efficient sampling method, data-driven Markov chain Monte Carlo (DDMCMC), which uses image observations for proposal probabilities. Knowledge of various aspects, including human shape, camera model, and image cues, are integrated in one theoretically sound framework. We present experimental results and quantitative evaluation, demonstrating that the resulting approach is effective for very challenging data. Index Terms—Multiple human segmentation, multiple human tracking, Markov chain Monte Carlo. Ç 1
A background layer model for object tracking through occlusion
- In Proc. ICCV, pages 1079– 1085, 2003. Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06) 0-7695-2521-0/06 $20.00 © 2006
"... Motion layer estimation has recently emerged as a promising object tracking method. In this paper, we extend previous research on layer-based tracker by introducing the concept of background occluding layers and explicitly inferring depth ordering of foreground layers. The background occluding layer ..."
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Cited by 47 (2 self)
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Motion layer estimation has recently emerged as a promising object tracking method. In this paper, we extend previous research on layer-based tracker by introducing the concept of background occluding layers and explicitly inferring depth ordering of foreground layers. The background occluding layers lie in front of, behind, and in between foreground layers. Each pixel in the background regions belongs to one of these layers and occludes all the foreground layers behind it. Together with the foreground ordering, the complete information necessary for reliably tracking objects through occlusion is included in our representation. An MAP estimation framework is developed to simultaneously update the motion layer parameters, the ordering parameters, and the background occluding layers. Experimental results show that under various conditions with occlusion, including situations with moving objects undergoing complex motions or having complex interactions, our tracking algorithm is able to handle many difficult tracking tasks reliably. 1
McMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Abstract—In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequate ..."
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Cited by 36 (1 self)
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Abstract—In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC. Index Terms—Markov chain Monte Carlo, QR factorization, updating, downdating, Rao-Blackwellized, particle filter, multitarget tracking, merged measurements, linear least squares, laser range scanner.
Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection
- in Proc. IEEE Conference on Computer Vision and Pattern Recognition
, 2007
"... Tracking objects using the mean shift method is performed by iteratively translating a kernel in the image space such that the past and current object observations are similar. Traditional mean shift method requires a symmetric kernel, such as a circle or an ellipse, and assumes constancy of the obj ..."
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Cited by 35 (0 self)
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Tracking objects using the mean shift method is performed by iteratively translating a kernel in the image space such that the past and current object observations are similar. Traditional mean shift method requires a symmetric kernel, such as a circle or an ellipse, and assumes constancy of the object scale and orientation during the course of tracking. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we present an object tracking method based on the asymmetric kernel mean shift, in which the scale and orientation of the kernel adaptively change depending on the observations at each iteration. Proposed method extends the traditional mean shift tracking, which is performed in the image coordinates, by including the scale and orientation as additional dimensions and simultaneously estimates all the unknowns in a few number of mean shift iterations. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not effected by the scale and orientation changes of the tracked objects; 3) it is less prone to the background clutter. 1.
