Basketball motion video target tracking algorithm based on improved gray neural network

Wang, Tianyi; Shi, Cuiping

doi:10.1007/s00521-022-07026-6

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…en the target tracking technology can provide this data directly [23]. In addition, for the referee on the field of play, there is a need for a variety of auxiliary information to ensure fair play, such as precise positioning of players, football trajectory analysis, and foul play recognition, in order to avoid potentially controversial calls during the game due to fierce competition.…”

Section: Introductionmentioning

confidence: 99%

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Peng

2022

Security and Communication Networks

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In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.

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Section: Introductionmentioning

confidence: 99%

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Peng

2022

Security and Communication Networks

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“…Deep-ID network in [15] taking into account both regional and global features It creates a pose estimation recognition methodology based on graph neural network by fusing the enhanced neural network as well as the interactive and collaborative model, but then just uses the algorithm to estimate the poses of several humans. The author in [16] integrates the upgraded gray neural network method with a basketball motion video target tracking technique. The experimental test findings suggest that this system can efficiently distinguish basketball movements having high recognition rate.…”

Section: Related Workmentioning

confidence: 99%

Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

2024

jes

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The use of information technology has currently resulted in a change in people's production and lifestyles as well as a catalyst for the sports industry's evolution. Currently, there is a growing use of digitization in the sports industry's basketball realm. Using 2D posture estimation, a novel lightweight deep learning architecture (LDLA) is built to accomplish the automatic analysis of basketball game footage. We offer a real-time method to identify numerous people's 2D poses in a video. Additionally, it examines how 2D pose estimation might be used to analyse basketball shooting videos. First, group and global motion features are extracted to represent semantic events. A full basketball game video is broken down into three stages: clip-based segmentation, classification of semantics occurrences utilizing audio and video, and temporal sequence characteristics employing GRU CNN. Video is first processed to create some basic video categorization and segmentation using the visual, movement, and auditory data. Further use of domain knowledge is made to find important events in the basketball video. Shot and image threshold recognition algorithms for videos have used both optical and kinematic prediction information; scene classification comes next. The placements of probable semantic events, such as "fouling" and "shooting at the basket," are then discovered by comparing the multidimensional data with supplementary domain expertise. Experimental results demonstrate the proposed LDLA method achieves 99.6% of accuracy, 93.2% of precision, 93.5% of recall and 89.5% of F1-score.

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“…They designed a system that utilizes DNNs to achieve accurate recognition of basketball teaching actions. Wang et al 25 proposed an improved grey neural network algorithm for target tracking in basketball motion videos. By optimizing the neural network's structure and parameters, they enhanced the accuracy and stability of target tracking.…”

Section: Recent Advances In Basketball Action Recognition Researchmentioning

confidence: 99%

Basketball technique action recognition using 3D convolutional neural networks

Wang,

Zuo,

Cordente Martínez

2024

Sci Rep

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This research investigates the recognition of basketball techniques actions through the implementation of three-dimensional (3D) Convolutional Neural Networks (CNNs), aiming to enhance the accurate and automated identification of various actions in basketball games. Initially, basketball action sequences are extracted from publicly available basketball action datasets, followed by data preprocessing, including image sampling, data augmentation, and label processing. Subsequently, a novel action recognition model is proposed, combining 3D convolutions and Long Short-Term Memory (LSTM) networks to model temporal features and capture the spatiotemporal relationships and temporal information of actions. This facilitates the facilitating automatic learning of the spatiotemporal features associated with basketball actions. The model’s performance and robustness are further improved through the adoption of optimization algorithms, such as adaptive learning rate adjustment and regularization. The efficacy of the proposed method is verified through experiments conducted on three publicly available basketball action datasets: NTURGB + D, Basketball-Action-Dataset, and B3D Dataset. The results indicate that this approach achieves outstanding performance in basketball technique action recognition tasks across different datasets compared to two common traditional methods. Specifically, when compared to the frame difference-based method, this model exhibits a significant accuracy improvement of 15.1%. When compared to the optical flow-based method, this model demonstrates a substantial accuracy improvement of 12.4%. Moreover, this method showcases strong robustness, accurately recognizing actions under diverse lighting conditions and scenes, achieving an average accuracy of 93.1%. The research demonstrates that the method reported here effectively captures the spatiotemporal relationships of basketball actions, thereby providing reliable technical assessment tools for basketball coaches and players.

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Basketball motion video target tracking algorithm based on improved gray neural network

Cited by 14 publications

References 23 publications

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

Basketball technique action recognition using 3D convolutional neural networks

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