Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network

Zuo, Kun; Su, Xin

doi:10.3390/electronics11223797

“…Basketball games involve multiple players moving around the court simultaneously, making it difficult to accurately track individual players and recognize their actions. Additionally, many actions in basketball may look similar but have different meanings depending on the context in which they occur [4,6]. For example, a player dribbling the ball may be preparing to pass, shoot, or drive to the basket, and distinguishing between these actions requires a deep understanding of the game and its strategies.…”

Section: Methodsmentioning

confidence: 99%

“…In fact, the sports science analysis industry is thriving nowadays which aims to monitor a player's performance, detect players, track their movement, and recognize the performed action. As a result, improving the scientific nature of coaches' training plans and analyzing athletes' performances using advanced technology like computer vision is crucial to enhancing the overall effectiveness of athletic training [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network

Khobdeh,

Yamaghani,

Sareshkeh

2023

J Supercomput

11

0

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The ability to identify human actions in uncontrolled surroundings is important for human-computer interaction (HCI), especially in the sports areas to offer athletes, coaches, and analysts valuable knowledge about movement techniques and aid referees in making well-informed decisions regarding sports movements. Noteworthy, recognizing human actions in the context of basketball sports remains a difficult task due to issues like intricate backgrounds, obstructed actions, and inconsistent lighting conditions. Accordingly, a method based on the combination of YOLO and deep fuzzy LSTM network is proposed in this paper. YOLO is utilized for detecting players in the frame and the combination of LSTM and fuzzy logic is used to perform the final classification. The reason behind using LSTM along with fuzzy logic refers to its inability in coping with uncertainty which led to the creation of a more transparent, interpretable, and accurate predictive system. The proposed model was validated on SpaceJam and Basketball-51 datasets. Based on the empirical results, the proposed model outperformed all baseline models on both datasets which obviously confirms the priority of our combinational model for basketball action recognition.

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“…Jisi and Yin [13] proposed a feature fusion network for student behaviour recognition, which was validated on UCF101 [21], HMDB51 [22], and on real student behaviour data in education. In an another study, authors [23] proposed a 3D attitude estimation algorithm using the RMPE (regional multi-person pose estimation) algorithm coupled with a deep neural network that combines human pose estimation and action recognition for basketball training. This algorithm was applied to a college sports basketball course to explore the influence of this teaching mode on classroom teaching effectiveness.…”

Section: Related Studymentioning

confidence: 99%

STAR-3D: A Holistic Approach for Human Activity Recognition in the Classroom Environment

Sharma,

Gupta,

Kumar

et al. 2024

Information

4

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The video camera is essential for reliable activity monitoring, and a robust analysis helps in efficient interpretation. The systematic assessment of classroom activity through videos can help understand engagement levels from the perspective of both students and teachers. This practice can also help in robot-assistive classroom monitoring in the context of human–robot interaction. Therefore, we propose a novel algorithm for student–teacher activity recognition using 3D CNN (STAR-3D). The experiment is carried out using India’s indigenously developed supercomputer PARAM Shivay by the Centre for Development of Advanced Computing (C-DAC), Pune, India, under the National Supercomputing Mission (NSM), with a peak performance of 837 TeraFlops. The EduNet dataset (registered under the trademark of the DRSTATM dataset), a self-developed video dataset for classroom activities with 20 action classes, is used to train the model. Due to the unavailability of similar datasets containing both students’ and teachers’ actions, training, testing, and validation are only carried out on the EduNet dataset with 83.5% accuracy. To the best of our knowledge, this is the first attempt to develop an end-to-end algorithm that recognises both the students’ and teachers’ activities in the classroom environment, and it mainly focuses on school levels (K-12). In addition, a comparison with other approaches in the same domain shows our work’s novelty. This novel algorithm will also influence the researcher in exploring research on the “Convergence of High-Performance Computing and Artificial Intelligence”. We also present future research directions to integrate the STAR-3D algorithm with robots for classroom monitoring.

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“…In fact, the sports science analysis industry is thriving nowadays which aims to monitor a player's performance, detect players, track their movement, and recognize the performed action. As a result, improving the scientific nature of coaches' training plans and analyzing athletes' performances using advanced technology like computer vision is crucial to enhancing the overall effectiveness of athletic training [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Basketball Action Recognition Based on the Combination of YOLO and a Deep Fuzzy LSTM Network

Khobdeh

¹

,

Yamaghani

²

,

Sareshkeh

³

2023

Preprint

View full text Add to dashboard Cite

The ability to identify human actions in uncontrolled surroundings is important for human-computer interaction (HCI), especially in the sports areas to offer athletes, coaches, and analysts valuable knowledge about movement techniques and aid referees in making well-informed decisions regarding sports movements. Noteworthy, recognizing human actions in the context of basketball sports remains a difficult task due to issues like intricate backgrounds, obstructed actions, and inconsistent lighting conditions. Accordingly, a method based on the combination of YOLO and deep fuzzy LSTM network is proposed in this paper. YOLO is utilized for detecting players in the frame and the combination of LSTM and fuzzy logic is used to perform the final classification. The reason behind using LSTM along with fuzzy logic refers to its inability in coping with uncertainty which led to the creation of a more transparent, interpretable, and accurate predictive system. The proposed model was validated on SpaceJam and Basketball-51 datasets. Based on the empirical results, the proposed model outperformed all baseline models on both datasets which obviously confirms the priority of our combinational model for basketball action recognition.

show abstract

Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network

Cited by 10 publications

References 31 publications

Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network

Basketball action recognition based on the combination of YOLO and a deep fuzzy LSTM network

STAR-3D: A Holistic Approach for Human Activity Recognition in the Classroom Environment

Basketball Action Recognition Based on the Combination of YOLO and a Deep Fuzzy LSTM Network

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