Abstract: Active player tracking in sports analytics is crucial for understanding team dynamics, player performance, and game strategies. This paper introduces an innovative approach to tracking active players in handball videos using a fusion of the Multi-Deep SORT algorithm and a Generative Adversarial Network (GAN) model. The novel integration aims to enhance player appearance for robust and precise tracking in dynamic gameplay. The system starts with a GAN model trained on annotated handball video data, generating synthetic frames to improve the visual quality and realism of player appearances, thereby refining the input data for tracking. The Multi-Deep SORT algorithm, enhanced with GAN-generated features, improves object association and continuous player tracking. This framework addresses key challenges in active player tracking, handling occlusions, variations in player appearances, and complex interactions. Additionally, GAN-based enhancements improve accuracy in distinguishing active from inactive players, facilitating precise localization and recognition. Performance evaluation demonstrates the system's efficacy in achieving high tracking accuracy, robustness, and differentiation between player activity levels. Metrics such as Average Precision (AP), Average Recall (AR), accuracy, and F1-score affirm the system's advancement in active player tracking. This pioneering fusion of Multi-Deep SORT with GAN-based player appearance enhancement sets a new standard for precise, robust, and context-aware active player tracking in handball videos. It offers comprehensive insights for coaches, analysts, and players to optimize team strategies and performance. This paper highlights the novel integration's advancements and benefits in the domain of sports analytics. Notably, the proposed method achieved enhanced efficiency with an average precision of 94.99%, recall of 93.67%, accuracy of 93.89%, and F-score of 94.33%.

Poovaraghan R J and Prabhavathy P. “Advanced Active Player Tracking System in Handball Videos Using Multi-Deep Sort Algorithm with GAN Approach”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01507116

@article{J2024,
title = {Advanced Active Player Tracking System in Handball Videos Using Multi-Deep Sort Algorithm with GAN Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01507116},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01507116},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Poovaraghan R J and Prabhavathy P}
}