Collaborative Training of Object Detection and Re-Identification in Multi-Object Tracking Using YOLOv8

Multi-object tracking (MOT) requires the integration of object detection and re-identification. While object detection entails identifying and precisely localizing objects in photos or videos, re-identification aligns obj ects across video frames. Together, these two approaches provide extensive insights on ob-ject movements and interactions in dynamic visual environments, which improves the overall performance of MOT systems. A wellliked object identification model that may be applied to MOT is YOLOv8. It can detect every object in an image in a single pass because it is a single-shot detector. It is hence ideal for real-time applications. By initially identifying items in each frame of a film, YOLOv8 can be utilized for MOT. A re-identification model is then used to compare the discovered objects to those in earlier frames. This enables the tracking of item trajectories throughout the whole video. High accuracy object tracking in films can be achieved by combining object detection with reidentification. This has a wide range of uses, including video surveillance, driverless cars, and sports analytics.