(I) Objective: Develop a system to track individuals in video sequences based on assigned ID numbers and estimate their key points for further analysis.

(II) Approach: Integrated YOLOv7-Pose for key point estimation, a ReID model for identity recognition, and ByteTrack for consistent tracking across video frames.

(III) Dataset: Trained on publicly available datasets, including COCO, CrowdPose, MPII, MHP, and AI Challenger 2017, to ensure robust performance across diverse scenarios.

(IV) Results:

The YOLOv7-Pose model accurately detected individuals and estimated their key points.

ByteTrack reliably tracked individuals with unique IDs assigned by the ReID model throughout the video.

The system demonstrated seamless performance in real-world video tracking and pose estimation applications.