BoT-SORT
Robust multi-object tracking combining motion and appearance cues.
About
BoT-SORT is a multi-object tracking method that associates detections across video frames by combining motion and appearance cues. It adds camera-motion compensation, an improved Kalman filter state vector, and an optional re-identification branch (BoT-SORT-ReID) for appearance matching. It reached state-of-the-art results on the MOT17 and MOT20 benchmarks and is commonly paired with YOLO detectors for tracking pipelines.
Reviews (0)
Leave a Review
No reviews yet. Be the first to review!
Details
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Intermediate (3/5)
- Minimum VRAM
- 4 GB
- Added
- Apr 3, 2026
Related Tools
Contrastive language-image pre-training model by OpenAI for zero-shot visual classification.
Lightweight face recognition and analysis framework wrapping multiple models.
Foundation model for monocular depth estimation by TikTok.
Monocular depth estimation model producing detailed depth maps from single images.
Meta AI research platform for object detection, segmentation, and pose estimation.
Simple and effective multi-object tracking using every detection box.