This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of MOT-16 challenge, and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes a raw video as input without any bells and whistles.
