Multi-target tracking algorithm based on deep learning

Huo, Wenxiao; Ou, Jin Ping; Li, Tianping

doi:10.1088/1742-6596/1948/1/012011

Cited by 5 publications

(4 citation statements)

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“…In recent years, with the rapid development of deep learning algorithms in the field of computers, vision, and recognition [ 14 ], many researchers apply the convolutional neural network algorithm to classification function, which can extract a large number of feature points to train the tracking model, so as to improve the performance of behavior recognition model [ 15 ]. In the deep learning algorithm, the twin network has the characteristics of sharing data, which is suitable for repetitive similar processing tasks.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes’ trajectory and behavior capture. Based on the target tracking algorithm, a regional proposal network RPN algorithm combined with the twin regional proposal network Siamese algorithm is proposed to study the tracking and recognition technology of athletes’ behavior. Then, the adaptive updating network is used to track the behavior target of athletes, and the simulation model of behavior recognition is established. This algorithm is different from the traditional twin network algorithm. It can accurately take the athlete’s behavior as the target candidate box in model training and reduce the interference of environment and other factors on model recognition. The results show that the Siamese-RPN algorithm can reduce the interference from the background and environment when tracking the athletes’ target behavior trajectory. This algorithm can improve the training behavior recognition model, ignore the background interference elements of the behavior image, and improve the accuracy and overall performance of the model. Compared with the traditional twin network method for sports behavior recognition, the Siamese-RPN algorithm studied in this paper can perform offline operations and distinguish the interference factors of athletes’ background environment. It can quickly capture the characteristic points of athletes’ behavior as the data input of the tracking model, so it has excellent popularization and application value.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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“…Occlusion is still among the existing challenge of MOT, because when the occlusion happens, it is challenging to predict the object's current position with only a simple tracking algorithm [16,17]. Occlusion mainly occurs in the frames that have many objects, which are solved by extracting appearance information using CNN [18][19][20] or using the graph information to find global attributes [21]. Huo's paper increased the resolution and appearance feature extraction resolution and robustness and then performed tracking by associating this data with the detection result [18].…”

Section: Occlusionmentioning

confidence: 99%

“…Occlusion mainly occurs in the frames that have many objects, which are solved by extracting appearance information using CNN [18][19][20] or using the graph information to find global attributes [21]. Huo's paper increased the resolution and appearance feature extraction resolution and robustness and then performed tracking by associating this data with the detection result [18].…”

Section: Occlusionmentioning

confidence: 99%

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Multiple Object Tracking in Deep Learning Approaches: A Survey

et al. 2021

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Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.

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