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Online Object Tracking: A Benchmark
"... Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly ..."
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Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field. 1.
Multiobject tracking as maximum weight independent set
- In Proc. IEEE Conf. on Computer Vision and Pattern Recognition
, 2011
"... This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best match ..."
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Cited by 42 (1 self)
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This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best matching tracklets that do not violate hard and soft contextual constraints between the resulting tracks. We prove that this data association problem can be formulated as finding the maximum-weight independent set (MWIS) of the graph. We present a new, polynomial-time MWIS algorithm, and prove that it converges to an optimum. Similarity and contextual constraints between object detections, used for data association, are learned online from object appearance and motion properties. Long-term occlusions are addressed by iteratively repeating MWIS to hierarchically merge smaller tracks into longer ones. Our results demonstrate advantages of simultaneously accounting for soft and hard contextual constraints in multitarget tracking. We outperform the state of the art on the benchmark datasets. 1.
Learning Occlusion with Likelihoods for Visual Tracking
"... We propose a novel algorithm to detect occlusion for visual tracking through learning with observation likelihoods. In our technique, target is divided into regular grid cells and the state of occlusion is determined for each cell using a classifier. Each cell in the target is associated with many s ..."
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Cited by 19 (3 self)
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We propose a novel algorithm to detect occlusion for visual tracking through learning with observation likelihoods. In our technique, target is divided into regular grid cells and the state of occlusion is determined for each cell using a classifier. Each cell in the target is associated with many small patches, and the patch likelihoods observed during tracking construct a feature vector, which is used for classification. Since the occlusion is learned with patch likelihoods instead of patches themselves, the classifier is universally applicable to any videos or objects for occlusion reasoning. Our occlusion detection algorithm has decent performance in accuracy, which is sufficient to improve tracking performance significantly. The proposed algorithm can be combined with many generic tracking methods, and we adopt L1 minimization tracker to test the performance of our framework. The advantage of our algorithm is supported by quantitative and qualitative evaluation, and successful tracking and occlusion reasoning results are illustrated in many challenging video sequences. 1.
Online learned discriminative part-based appearance models for multi-human tracking
- In ECCV
, 2012
"... Abstract. We introduce an online learning approach to produce discriminative part-based appearance models (DPAMs) for tracking multiple humans in real scenes by incorporating association based and category free tracking methods. Detection responses are gradually associated into tracklets in multiple ..."
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Cited by 18 (0 self)
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Abstract. We introduce an online learning approach to produce discriminative part-based appearance models (DPAMs) for tracking multiple humans in real scenes by incorporating association based and category free tracking methods. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous multi-target tracking approaches which do not explicitly consider occlusions in appearance modeling, we introduce a part based model that explicitly finds unoccluded parts by occlusion reasoning in each frame, so that occluded parts are removed in appearance modeling. Then DPAMs for each tracklet is online learned to distinguish a tracklet with others as well as the background, and is further used in a conservative category free tracking approach to partially overcome the missed detection problem as well as to reduce difficulties in tracklet associations under long gaps. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods. Key words: multi-human tracking, online learned discriminative models 1
Continuous Energy Minimization for Multi-Target Tracking
"... Abstract—Many recent advances in multiple target tracking aim at finding a (nearly) optimal set of trajectories within a temporal window. To handle the large space of possible trajectory hypotheses, it is typically reduced to a finite set by some form of data-driven or regular discretization. In thi ..."
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Cited by 18 (3 self)
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Abstract—Many recent advances in multiple target tracking aim at finding a (nearly) optimal set of trajectories within a temporal window. To handle the large space of possible trajectory hypotheses, it is typically reduced to a finite set by some form of data-driven or regular discretization. In this work we propose an alternative formulation of multi-target tracking as minimization of a continuous energy. Contrary to recent approaches, we focus on designing an energy that corresponds to a more complete representation of the problem, rather than one that is amenable to global optimization. Besides the image evidence, the energy function takes into account physical constraints, such as target dynamics, mutual exclusion, and track persistence. In addition, partial image evidence is handled with explicit occlusion reasoning, and different targets are disambiguated with an appearance model. To nevertheless find strong local minima of the proposed non-convex energy we construct a suitable optimization scheme that alternates between continuous conjugate gradient descent and discrete trans-dimensional jump moves. These moves, which are executed such that they always reduce the energy, allow the search to escape weak minima and explore a much larger portion of the search space of varying dimensionality. We demonstrate the validity of our approach with an extensive quantitative evaluation on several public datasets. Index Terms—Multi-object tracking, tracking-by-detection, visual surveillance, continuous optimization. F 1
Online spatio-temporal structural context learning for visual tracking
- in Proc. 12th ECCV
"... Abstract. Visual tracking is a challenging problem, because the target frequently change its appearance, randomly move its location and get occluded by other ob-jects in unconstrained environments. The state changes of the target are tempo-rally and spatially continuous, in this paper therefore, a r ..."
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Cited by 11 (3 self)
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Abstract. Visual tracking is a challenging problem, because the target frequently change its appearance, randomly move its location and get occluded by other ob-jects in unconstrained environments. The state changes of the target are tempo-rally and spatially continuous, in this paper therefore, a robust Spatio-Temporal structural context based Tracker (STT) is presented to complete the tracking task in unconstrained environments. The temporal context capture the historical ap-pearance information of the target to prevent the tracker from drifting to the back-ground in a long term tracking. The spatial context model integrates contributors, which are the key-points automatically discovered around the target, to build a supporting field. The supporting field provides much more information than ap-pearance of the target itself so that the location of the target will be predicted more precisely. Extensive experiments on various challenging databases demon-strate the superiority of our proposed tracker over other state-of-the-art trackers.
Using 3D Scene Structure to Improve Tracking
"... In this work we consider the problem of tracking objects from a moving airborne platform in wide area surveillance through long occlusions and/or when their motion is unpredictable. The main idea is to take advantage of the known 3D scene structure to estimate a dynamic occlusion map, and to use the ..."
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Cited by 3 (1 self)
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In this work we consider the problem of tracking objects from a moving airborne platform in wide area surveillance through long occlusions and/or when their motion is unpredictable. The main idea is to take advantage of the known 3D scene structure to estimate a dynamic occlusion map, and to use the occlusion map to determine traffic entry and exit into these zones, which we call sources and sinks. Then the track linking problem is formulated as an alignment of sequences of tracks entering a sink and leaving a source. The sequence alignment problem is solved optimally and efficiently using dynamic programming. We have evaluated our algorithm on a vehicle tracking task in wide area motion imagery and have shown that track fragmentation is significantly decreased and outperforms the Hungarian algorithm. 1.
Part-Based Online Tracking With Geometry Constraint and Attention Selection
- IEEE Trans. Circuits Syst. Video Technol
"... Abstract—Visual tracking in condition of occlusion, appear-ance or illumination change has been a challenging task over decades. Recently, some online trackers, based on the detection by classification framework, have achieved good performance. However, problems are still embodied in at least one of ..."
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Cited by 3 (2 self)
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Abstract—Visual tracking in condition of occlusion, appear-ance or illumination change has been a challenging task over decades. Recently, some online trackers, based on the detection by classification framework, have achieved good performance. However, problems are still embodied in at least one of the three aspects: 1) tracking the target with a single region has poor adaptability for occlusion, appearance or illumination change; 2) lack of sample weight estimation, which may cause overfitting issue; and 3) inadequate motion model to prevent target from drifting. For tackling the above problems, this paper presents the contributions as follows: 1) a novel part-based structure is utilized in the online AdaBoost tracking; 2) attentional sample weighting and selection is tackled by introducing a weight relaxation factor, instead of treating the samples equally as traditional trackers do; and 3) a two-stage motion model, multiple parts constraint, is proposed and incorporated into the part-based structure to ensure a stable tracking. The effectiveness and efficiency of the proposed tracker is validated upon several complex video sequences, compared with seven popular online trackers. The experimental results show that the proposed tracker can achieve increased accuracy with comparable computational cost. Index Terms—Attention selection, multiple parts constraint, object tracking, online AdaBoost (OAB), relaxation factor. I.
Dynamic Context for Tracking behind Occlusions
"... Abstract. Tracking objects in the presence of clutter and occlusion remains a challenging problem. Current approaches often rely on aprioritarget dynamics and/or use nearly rigid image context to determine the target position. In this paper, a novel algorithm is proposed to estimate the location of ..."
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Abstract. Tracking objects in the presence of clutter and occlusion remains a challenging problem. Current approaches often rely on aprioritarget dynamics and/or use nearly rigid image context to determine the target position. In this paper, a novel algorithm is proposed to estimate the location of a target while it is hidden due to occlusion. The main idea behind the algorithm is to use contextual dynamical cues from multiple supporter features which may move with the target, move independently of the target, or remain stationary. These dynamical cues are learned directly from the data without making prior assumptions about the motions of the target and/or the support features. As illustrated through several experiments, the proposed algorithm outperforms state of the art approaches under long occlusions and severe camera motion.
Robust Superpixel Tracking via Depth Fusion
"... Abstract—Although numerous trackers have been designed to adapt to the nonstationary image streams that change over time, it remains a challenging task to facilitate a tracker to accurately distinguish the target from the background in every frame. This paper proposes a robust superpixel-based track ..."
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Cited by 2 (0 self)
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Abstract—Although numerous trackers have been designed to adapt to the nonstationary image streams that change over time, it remains a challenging task to facilitate a tracker to accurately distinguish the target from the background in every frame. This paper proposes a robust superpixel-based tracker via depth fusion, which exploits the adequate structural information and great flexibility of mid-level features captured by superpixels, as well as the depth-map’s discriminative ability for the target and background separation. By introducing graph-regularized sparse coding into the appearance model, the local geometrical structure of data is considered, and the resulting appearance model has a more powerful discriminative ability. Meanwhile, the similarity of the target superpixels ’ neighborhoods in two adjacent frames is also incorporated into the refinement of the target estimation, which helps a more accurate localization. Most importantly, the depth cue is fused into the superpixel-based target estimation so as to tackle the cluttered background with similar appearance to the target. To evaluate the effectiveness of the proposed tracker, four video sequences of different challenging situations are contributed by the authors. The comparison results demonstrate that the proposed tracker has more robust and accurate performance than seven ones representing the state-of-the-art. Index Terms—Computer vision, depth fusion, graph regular-ized sparse coding, object tracking, segmentation, superpixel. I.
