A Robust Multi-Athlete Tracking Algorithm by Exploiting Discriminant Features and Long-Term Dependencies

Ran, Nan; Kong, Longteng; Wang, Yunlong; Liu, Qingjie

doi:10.1007/978-3-030-05710-7_34

Cited by 28 publications

(20 citation statements)

References 16 publications

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“…Ran et al [52] proposed a combination of a classical CNN for visual features extraction and AlphaPose CNN for pose estimation. The output of these two networks was then fed into a LSTM model together with the tracklet information history to compute a similarity, as it is explained in section 3.3.1.…”

Section: Cnns As Visual Feature Extractorsmentioning

confidence: 99%

“…In the following years, other works have taken advantage of the detection accuracy of Faster R-CNN, that has since been applied as part of MOT algorithms to detect athletes [52], cells [53] and pigs [54]. Moreover, an adaptation of Faster R-CNN that adds a segmentation branch, Mask R-CNN [17], has been used for example by Zhou et al [55] both to detect and to track pedestrians,…”

Section: Yu Et Al Reached the Same Conclusion Inmentioning

confidence: 99%

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Deep learning in video multi-object tracking: A survey

et al. 2020

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The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learning, the algorithms that provide a solution to this problem have benefited from the representational power of deep models. This paper provides a comprehensive survey on works that employ Deep Learning models to solve the task of MOT on single-camera videos. Four main steps in MOT algorithms are identified, and an in-depth review of how Deep Learning was employed in each one of these stages is presented. A complete experimental comparison of the presented works on the three MOTChallenge datasets is also provided, identifying a number of similarities among the top-performing methods and presenting some possible future research directions.In this section, a general description about the problem of MOT is provided. The main characteristics and common steps of MOT algorithms are identified and described in section 2.1. The metrics that are usually employed to evaluate the performance of the models are discussed in section 2.2, while the most important benchmark datasets are presented in section 2.3. 7 The website says the detections were obtained using a model based on a latent SVM, or L-SVM. That model is now known as Deformable Parts Model (DPM). 8

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Section: Cnns As Visual Feature Extractorsmentioning

confidence: 99%

Section: Yu Et Al Reached the Same Conclusion Inmentioning

confidence: 99%

Deep learning in video multi-object tracking: A survey

et al. 2020

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“…Data association is closely related to the similarity measurement of targets. Currently, some MOT systems adopt a deep model in the detection and similarity measurement stages [39][40][41][42].…”

Section: Mot Based On Detection Drivenmentioning

confidence: 99%

Real-Time Multiobject Tracking Based on Multiway Concurrency

Gong

et al. 2021

Sensors

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This paper explored a pragmatic approach to research the real-time performance of a multiway concurrent multiobject tracking (MOT) system. At present, most research has focused on the tracking of single-image sequences, but in practical applications, multiway video streams need to be processed in parallel by MOT systems. There have been few studies on the real-time performance of multiway concurrent MOT systems. In this paper, we proposed a new MOT framework to solve multiway concurrency scenario based on a tracking-by-detection (TBD) model. The new framework mainly focuses on concurrency and real-time based on limited computing and storage resources, while considering the algorithm performance. For the former, three aspects were studied: (1) Expanded width and depth of tracking-by-detection model. In terms of width, the MOT system can support the process of multiway video sequence at the same time; in terms of depth, image collectors and bounding box collectors were introduced to support batch processing. (2) Considering the real-time performance and multiway concurrency ability, we proposed one kind of real-time MOT algorithm based on directly driven detection. (3) Optimization of system level—we also utilized the inference optimization features of NVIDIA TensorRT to accelerate the deep neural network (DNN) in the tracking algorithm. To trade off the performance of the algorithm, a negative sample (false detection sample) filter was designed to ensure tracking accuracy. Meanwhile, the factors that affect the system real-time performance and concurrency were studied. The experiment results showed that our method has a good performance in processing multiple concurrent real-time video streams.

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“…A preliminary version of this work is presented in [ 10 ]. Compared with the conference version, the extensions include: (1) more details of the proposed approach and related works are explained; (2) more experiments on APIDIS and NCAA databases are conducted and more results are displayed and discussed.…”

Section: Introductionmentioning

confidence: 99%

Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies

Kong

Zhu

Ran

et al. 2020

Sensors

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This paper addresses the Multi-Athlete Tracking (MAT) problem, which plays a crucial role in sports video analysis. There exist specific challenges in MAT, e.g., athletes share a high similarity in appearance and frequently occlude with each other, making existing approaches not applicable for this task. To address this problem, we propose a novel online multiple athlete tracking approach which make use of long-term temporal pose dynamics for better distinguishing different athletes. Firstly, we design a Pose-based Triple Stream Network (PTSN) based on Long Short-Term Memory (LSTM) networks, capable of modeling long-term temporal pose dynamics of athletes, including pose-based appearance, motion and athletes’ interaction clues. Secondly, we propose a multi-state online matching algorithm based on bipartite graph matching and similarity scores produced by PTSN. It is robust to noisy detections and occlusions due to the reliable transitions of multiple detection states. We evaluate our method on the APIDIS, NCAA Basketball and VolleyTrack databases, and the experiment results demonstrate its effectiveness.

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A Robust Multi-Athlete Tracking Algorithm by Exploiting Discriminant Features and Long-Term Dependencies

Cited by 28 publications

References 16 publications

Deep learning in video multi-object tracking: A survey

Deep learning in video multi-object tracking: A survey

Real-Time Multiobject Tracking Based on Multiway Concurrency

Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies

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