mmtracking

Why we included this project

Following objects across frames is a different problem from detecting them in a single image, and MMTracking covers all four of its main variants in one consistent toolbox: video object detection, multiple object tracking, single object tracking, and video instance segmentation. Because the project is part of OpenMMLab and built on PyTorch, it reuses detectors from MMDetection and lets you assemble a pipeline from configuration files, so swapping a tracking algorithm rarely means rewriting training or inference code. The model zoo includes widely used methods, from classic online trackers like DeepSORT, ByteTrack, and OC-SORT to transformer-based ones such as STARK and MixFormer, with results reported on standard benchmarks like MOT Challenge, LaSOT, and YouTube-VIS. That makes it a sensible starting point whether you are prototyping a tracker for a camera-based application or reproducing published results in research.