deep_sort_pytorch
Discover how this project leverages state-of-the-art detection technologies in multi-object tracking, offering CNN-based feature extraction and compatibility with detectors such as YOLOv3, YOLOv5, and Mask R-CNN. Experience notable advancements in MOT tracking through GPU-accelerated NMS, batch processing, and improved computational efficiency. The project includes bug fixes, code refactoring, and introduces new features for multi-tracking such as category, ID, and target mask display. It provides a streamlined framework for effective single and distributed GPU training, enabling robust performance in object tracking applications.
