Hybrid-Based Multi-Object Tracking for Football Sport - Htun, Zin Mar; Theingi Myint (AI Society & STMIK Indonesia, 2025)
Tracking is now popular in real world. Precise tracking of objects in real-time videos is a challenging task. With billions of fans, football is a rapidly expanding sport that has proven essential to many nations and their citizens in particular. None of the numerous great target tracking algorithms have surfaced in recent years primarily deep learning and correlation filtering that can track players in soccer game videos with high accuracy. In this paper, the proposed system is used You Only Look Once version 8-nano (YOLOv8n) for Multi-Object Detection (MOD) to get higher detection accuracy results. Moreover, this system is based on the hybrid method for tracking. The hybrid method is combined with stacked Long Short Term Memory (LSTM) and Fairness of Detection and Re-identification in Multipe Object Tracking (FairMOT). The experimental analysis shows that the proposed system is efficiently and better accuacy because the best detection results with YOLOv8n is 93% for precision, 91% for recall and 92% for mAP(50) with own dataset. After using the proposed system, the average of the Multi Object Tracking Accuracy (MOTA) is 80 % at IoU-Threshold 0.5, the average of the Multi Object Tracking Precision (MOTP) is 89% at IoU-Threshold 0.8 and the average of the final mAP is 96% at IoU- Threshold 0.5 by using hybrid method for tracking.