Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking

Bae, Seung-Hwan; Yoon, Kuk-Jin

doi:10.1109/tpami.2017.2691769

Cited by 231 publications

(182 citation statements)

References 41 publications

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“…Visesh Chari et al proposed [4] in 2015 to add pairwise costs to the min-cost network flow framework and design a convex relaxation solution with an efficient rounding heuristic which empirically gives certificates of small suboptimality. In 2017, Seung-Hwan Bae et al [3]proposed a robust online MOT method which uses confidence-based data association to handle track fragments due to occlusion or unreliable detections.…”

Section: Related Workmentioning

confidence: 99%

Video abstract system based on spatial-temporal neighborhood trajectory analysis algorithm

Huang

Cai

et al. 2018

Multimed Tools Appl

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In this paper, a video abstract system based on spatial-temporal neighborhood trajectory analysis algorithm which is mainly used to process surveillance videos is proposed. The algorithm uses the spatial adjacency of foreground targets and tracks the spatialtemporal neighboring moving targets to get their whole trajectories in order to meet the requirement of processing speed and accuracy. The indicators consist of trajectory detection rate, trajectory tracking average continuity and video abstract processing speed are used to evaluate the effectiveness of the system. We compare the algorithm with the other three algorithms, and the results show that spatial-temporal neighborhood trajectory analysis algorithm has sufficient trajectory detection rate and processing speed for surveillance video abstraction.

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Section: Related Workmentioning

confidence: 99%

Video abstract system based on spatial-temporal neighborhood trajectory analysis algorithm

Huang

Cai

et al. 2018

Multimed Tools Appl

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“…Many state-of-the-art people trackers 5,[14][15][16][17][18][19][20][21]42 use the PETS2009 S2.L1 video sequence to evaluate their work in terms of aforementioned MOTA and MOTP matrices. In the computation of MOTA and MOTP, all trackers use the same ground truth, 39 except in Refs.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…13 The evaluation results show that deploying our occlusion handling on the baseline tracker 10 considerably improves the accuracy. On the S2.L1 video of the PETS2009 dataset, which is widely used as a benchmark in literature, our tracker outperforms state-ofthe-art techniques 5,[14][15][16][17][18][19][20][21] in terms of multiple object tracking accuracy (MOTA).…”

Section: Introductionmentioning

confidence: 99%

Occlusion handling framework for tracking in smart camera networks by per-target assistance task assignment

Deboeverie

Veelaert

et al. 2017

J. Electron. Imaging

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Abstract. Occlusion is one of the most difficult challenges in the area of visual tracking. We propose an occlusion handling framework to improve the performance of local tracking in a smart camera view in a multicamera network. We formulate an extensible energy function to quantify the quality of a camera's observation of a particular target by taking into account both person-person and object-person occlusion. Using this energy function, a smart camera assesses the quality of observations over all targets being tracked. When it cannot adequately observe of a target, a smart camera estimates the quality of observation of the target from view points of other assisting cameras. If a camera with better observation of the target is found, the tracking task of the target is carried out with the assistance of that camera. In our framework, only positions of persons being tracked are exchanged between smart cameras. Thus, communication bandwidth requirement is very low. Performance evaluation of our method on challenging video sequences with frequent and severe occlusions shows that the accuracy of a baseline tracker is considerably improved. We also report the performance comparison to the state-of-the-art trackers in which our method outperforms.

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“…On the other hand, since the online approach cannot apply the global optimization models, intensive motion analysis and appearance feature learning have been popularly utilized with a hierarchical data association framework and the online Bayesian model [14], [15], [18], [21], [23], [25], [37], [42]. Yoon et al [23] proposed a relative motion analysis between all objects in a frame, and then improved the work [23] by adding the cost optimization function using context constraints in [21].…”

mentioning

confidence: 99%

“…Bae et al [25] exploited the incremental linear discriminant analysis (LDA) for appearance learning and presented a tracklet confidence based data association framework. Also, in [14], they improved their previous work [25] by using the deep neural network (DNN) based appearance learning instead of the incremental LDA. As we addressed in the previous paragraph, DNN has given breakthrough in appearance learning i.e., object classification and detection.…”

mentioning

confidence: 99%

Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management

Song

Yoon

et al. 2019

IEEE Access

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In this paper, we propose an efficient online multiobject tracking framework based on the Gaussian mixture probability hypothesis density (GMPHD) filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection. The hierarchical data association consists of two steps: detection-to-track and track-to-track associations, which can recover the lost tracks and their switched IDs. In addition, the proposed framework is equipped with an object grouping management scheme which handles occlusion problems with two main parts. The first part is "track merging" which can merge the false positive tracks caused by false positive detections from occlusions, where the false positive tracks are usually occluded with a measure. The measure is the occlusion ratio between visual objects, sum-of-intersection-over-area (SIOA) we defined instead of intersection-over-union (IOU) metric. The second part is "occlusion group energy minimization (OGEM)" which prevents the occluded true positive tracks from false "track merging". We define each group of the occluded objects as an energy function and find an optimal hypothesis which makes the energy minimal. We evaluate the proposed tracker in benchmark datasets such as MOT15 and MOT17 which are built for multi-person tracking. An ablation study in training dataset shows that not only "track merging" and "OGEM" complement each other but also the proposed tracking method has more robust performance and less sensitive to parameters than baseline methods. Also, SIOA works better than IOU for various sizes of false positives. Experimental results show that the proposed tracker efficiently handles occlusion situations and achieves competitive performance compared to the state-of-theart methods. Especially, our method shows the best multi-object tracking accuracy among the online and real-time executable methods.

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Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking

Cited by 231 publications

References 41 publications

Video abstract system based on spatial-temporal neighborhood trajectory analysis algorithm

Video abstract system based on spatial-temporal neighborhood trajectory analysis algorithm

Occlusion handling framework for tracking in smart camera networks by per-target assistance task assignment

Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management

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