Authors - Selvamani K, Kanimozhi S, Muthusundar S K, Saranraj S, Jagadeesh K Abstract - Multi-object tracking (MOT) is a pillar of many computer vision applications such as video surveillance, self-driving and crowd analysis [1]. The main difficulty does not only exist in correct identification of objects but also in consistent identities of objects in different frames when there is occlusion, camera motion and changes in scene density [14]. The paper introduces a highly advanced MOT system, combining the latest YOLOv8x detector with a modified and improved version of the original ByteTrack association system, which is called RobustBoTSORTTracker [14]. With the new detection quality of YOLOv8x and the robustness of low-confidence detections in ByteTrack, augmented with selective improvements of BoT-SORT including camera motion compensation and exponential moving average smoothing, the proposed system demonstrates significant gains on the MOT15 benchmark [7]. Experimental findings indicate a MOTA of 55.6, IDF1 of 72.2, precision of 74.3 and a recall of 95.7, which is significantly higher than the previous baselines under similar conditions.
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