Precision in Motion: Comparative Analysis of YOLOvX Versions for Real-Time Multi-Object Tracking

Real-time object tracking is crucial in the world of computer vision. The real-world application of tracking in traffic management systems is quite challenging, and traditional methods have limitations due to speed and accuracy. This paper aims to explore You Only Look Once (YOLO)v8, a recent variant of YOLO, along with the deepsort tracking algorithm. Furthermore, a comparison is performed for YOLOv8 with key models-YOLOv3 and YOLOv4. Emphasizing accuracy, speed, and robustness, the study introduces new method at the crossroads of advanced object detection and dynamic tracking. We employed YOLOv8 on two datasets, COCO128 and a vehicle dataset, attaining Mean Average Precision (mAP) of 0.77 and 0.88 on the vehicle and COCO128 dataset, respectively. Rigorous evaluation provides valuable insights, guiding practical advancements in computer vision.