[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan, Chongdi

doi:10.1155/2021/5973531

Cited by 7 publications

(6 citation statements)

References 28 publications

(29 reference statements)

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“…Following the determination of the objective box options, the tracker estimates the multitarget motion limits of the target movement video using an optic streaming approach based on the multitarget movement of the object in the interframe movement video. e probe initially sweeps each moving picture frames frame by frame and looks at subregions of simultaneously detected and learned frames frame by frame until the present instant is very similar to the objective to be pursued [7]. To sum up, most of the above literature are about deep learning, multitarget tracking algorithms, and motion trajectory recognition.…”

Section: Related Workmentioning

confidence: 99%

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Song

Zhao

2022

Computational Intelligence and Neuroscience

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Freestyle skiing U-shaped field is a snow sport that uses double boards to perform a series of action skills in a U-shaped pool, which requires very high skills for athletes. In this era of deep learning, in order to develop a more scientific training method, this paper combines multitarget tracking algorithm and deep learning to conduct research in freestyle skiing U-shaped venue skills motion capture. Therefore, this paper combines the convolutional neural network and multitarget tracking algorithm in deep learning to study the human action recognition technology, and then uses the LSTM module to study the freestyle skiing U-shaped venue skills. Finally, this paper designs the training method of the action recognition algorithm of the freestyle U-shaped skiing skills multitarget tracking algorithm based on deep learning. This paper also designs multitarget tracking dataset experiments and model updating experiments. Based on the data of experimental analysis, the training method designed in this paper is optimized, and finally compared with the traditional training method. Compared with the traditional freestyle U-shaped skiing skills training method, the experimental results show that the training method of the freestyle U-shaped skiing skills multitarget tracking algorithm action recognition algorithm is based on deep learning designed in this paper and this improves the skill score by 14.48%. Most professional students are very satisfied with the training method designed in this paper.

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Section: Related Workmentioning

confidence: 99%

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Song

Zhao

2022

Computational Intelligence and Neuroscience

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“…Count and score this movement according to the distance between jaw and crossbar and the angle of arm bending. Combine Kinect cheap and powerful somatosensory equipment with mass sports to create a brand-new sports teaching method and promote the rapid development of mass sports [13,14]. We firmly believe that through our persistent efforts, we will definitely realize a high-quality sports teaching system that meets the ideal conditions.…”

Section: Introductionmentioning

confidence: 94%

A Novel Automatic Tracking Method of Moving Image Sequence Marker Points Uses Kinect and Wireless Network Technology

Chen

Lin

2022

Wireless Communications and Mobile Computing

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Sports biomechanics research includes the use of various technical methods to collect various sports biomechanical parameters and variables. Sports biomechanics is one of the branches of sports science, which is a new science for studying sports. In the process of teaching, the Kinect somatosensory camera is used for bone tracking to obtain spatial coordinate information of human nodes, and the teaching data for imitation learning is obtained after preprocessing. This paper builds a general sports auxiliary training system and designs some of the system’s functions in detail, using the advantages of Microsoft’s Kinect collector in image acquisition. The automatic tracking method of sports image sequence marker points is studied in this paper using Kinect. Kinect is used to collect bone point information, establish a virtual coordinate system, obtain each joint vector from the node information, calculate the included angle between the vectors, and then obtain the control angle of each joint, providing data support for imitation representation learning. Count and score this movement based on the distance between the jaw and the crossbar as well as the angle at which the arm bends.

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“…When the video was very long, the front-end features could not build sufficient correlation, which made the accuracy of judging for the action decrease. For motion target tracking, Duan [ 28 ] applied a deep neural network to multi-target the motion tracking in the sports video. After the target frame selection was determined, the detector scanned each motion video frame one after another, and then observed the previously discovered subregions and learned image frames until the current moment could highly resemble the target to be tracked.…”

Section: Related Workmentioning

confidence: 99%

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Liu

et al. 2023

Sensors

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Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store & return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

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[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Cited by 7 publications

References 28 publications

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

A Novel Automatic Tracking Method of Moving Image Sequence Marker Points Uses Kinect and Wireless Network Technology

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

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