Welcome to MOTChallenge: The Multiple Object Tracking Benchmark!

In the recent past, the computer vision community has relied on several centralized benchmarks for performance evaluation of numerous tasks including object detection, pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state-of-the-art in the respective research fields. Interestingly, there has been rather limited work on the standardization of multiple target tracking evaluation. One of the few exceptions is the well-known PETS dataset, targeted primarily at surveillance applications. Even for this widely used benchmark, a common technique for presenting tracking results to date involves using different subsets of the available data, inconsistent model training and varying evaluation scripts.
With this benchmark we would like to pave the way for a unified framework towards more meaningful quantification of multi-target tracking.

What do we provide?

We have created a framework for the fair evaluation of multiple people tracking algorithms. In this framework we provide:

We rely on the spirit of crowdsourcing, and we encourage researchers to submit their sequences to our benchmark, so the quality of multiple object tracking systems can keep increasing and tackling more challenging scenarios.