A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

Yang, Menglong; Liu, Yiguang; Wen, Longyin; Zhou, You; Li, Stan Z.

doi:10.1109/cvpr.2014.169

Cited by 25 publications

(21 citation statements)

References 19 publications

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“…Numerous multi-object tracking methods formulated the task as the state estimation problem using the filter based strategies, such as Kalman filter [38,36] and particle filter [29,32,39,63]. These methods typically predicted the states of targets in short time durations which did not perform well in complex scenarios.…”

Section: Survey Of the Existing Mot Methodsmentioning

confidence: 99%

UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking

Wen¹,

Cai

et al. 2020

Computer Vision and Image Understanding

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475

258

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In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DE-Tection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140, 000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.

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Section: Survey Of the Existing Mot Methodsmentioning

confidence: 99%

UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking

Wen¹,

Cai

et al. 2020

Computer Vision and Image Understanding

Self Cite

475

258

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show abstract

“…Discrete-continuous optimization techniques have been proposed [3,38], where discrete (data association) and continuous (trajectory fitting) optimization problems are solved in an alternating way until converging to a hopefully better local minimum. In [7,11,15,17,59], approximate Markov chain Monte Carlo techniques are employed for solving the data association problem. In order to increase the discriminative power of appearance and dynamical models, online learning approaches have been suggested [33, 35, 46, 56-58, 60, 62].…”

Section: Related Workmentioning

confidence: 99%

FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

Lenz

Geiger

Urtasun

2015

2015 IEEE International Conference on Computer Vision (ICCV)

100

106

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One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in significantly faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrarily length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.

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“…The MH algorithm [19] has been applied in the multiple target tracking task [21,23], where the authors use it for particle filter sampling to identify the state of each target precisely. However, in our paper, we do not aim at identifying each part all the time.…”

Section: Optimal Parts Inferencementioning

confidence: 99%

Learning Discriminative Hidden Structural Parts for Visual Tracking

Wen

Cai

et al. 2015

Computer Vision - ACCV 2014 Workshops

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Part-based visual tracking is attractive in recent years due to its robustness to occlusion and non-rigid motion. However, how to automatically generate the discriminative structural parts and consider their interactions jointly to construct a more robust tracker still remains unsolved. This paper proposes a discriminative structural part learning method while integrating the structure information, to address the visual tracking problem. Particulary, the state (e.g. position, width and height) of each part is regarded as a hidden variable and inferred automatically by considering the inner structure information of the target and the appearance difference between the target and the background. The inner structure information considering the relationship between neighboring parts, is integrated using a graph model based on a dynamically constructed pair-wise Markov Random Field. Finally, we adopt Metropolis-Hastings algorithm integrated with the online Support Vector Machine to complete the hidden variable inference task. The experimental results on various challenging sequences demonstrate the favorable performance of the proposed tracker over the state-of-the-art ones.

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A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

Cited by 25 publications

References 19 publications

UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking

UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking

FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

Learning Discriminative Hidden Structural Parts for Visual Tracking

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