Tracking Socer Player Based on Deepsort Algorithm with YOLOV8 FrameWork
Abstract
anidentificationtoacertainobjectandsubsequentlycon-
sistently recognizing that object without altering the assigned
identification over a sequence of frame images and associating
itaccordingly.Whenperformingresearchonobjecttracking,
especially in sports where the object of interest is a human, a
resilient technology is necessary to facilitate the tracking process.
When the state-of-the-art object detection approach, YOLOV8,
is combined with the DeepSORT algorithm, it is anticipated to
produce highly accurate and exact outcomes in the tracking
and detection of objects. Challenges in multi-object tracking
include robustness, oculusion, and identity shifts. In our research,
we take advantage of a fusion of YOLOV8 and DeepSORT
algorithmstoachieveahighlyreliableandprecisetracking
solution. The implementation of the Kalman filter-based motion
prediction in DeepSORT allows for the achievement of smooth
trajectories, whereas the YOLOV8 deep neural network used
assists in precisely recognizing the appearance of objects on the
field. The result of our experiment shown the tracking we get is
38% HOTA, 47% DetA, 31% AssA, 68% DetPre, 35% AssRE,
61% AssPr amd 79% LOcA.
Index Terms—Tracking, DeepSORT, YOLO, MOT, Socce
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