FoundationVision/ByteTrack
ByteTrack
ByteTrack is a multi-object tracking framework that implements the ByteTrack algorithm, an ECCV 2022 method designed to recover occluded objects and reduce trajectory fragmentation. The core innovation of the project is its association algorithm, which processes every detection box—including low-confidence ones—by using separate high and low score thresholds, Kalman filter motion prediction, and Hungarian algorithm matching to produce consistent object identities across video frames.
The project distinguishes itself by its comprehensive approach to handling occlusions and fragmented trajectories through low-score detection recovery, where low-confidence detections are re-matched with unmatched tracklets. It includes a complete tracklet lifecycle management system that creates, updates, and deletes tracklets based on association results and a configurable maximum lost frame count. ByteTrack also provides model export capabilities, allowing trained tracking models to be converted to ONNX, TensorRT, ncnn, or DeepStream formats for production inference deployment.
The framework supports custom tracker training through a fine-tuning pipeline for adapting pretrained detectors to custom multi-object tracking datasets. It processes video files frame by frame, integrating detection results from any object detector to produce bounding boxes and tracked object identities as output. The project includes ONNX model export and TensorRT inference optimization with FP16 quantization for low-latency GPU inference.
