In the recent past, the computer vision community has developed centralized
benchmarks for the performance evaluation of a variety of tasks, including
generic object and 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 area. Interestingly, there has
been rather limited work on the standardization of quantitative benchmarks for
multiple target tracking. One of the few exceptions is the well-known PETS
dataset, targeted primarily at surveillance applications. Despite being widely
used, it is often applied inconsistently, for example involving using different
subsets of the available data, different ways of training the models, or
differing evaluation scripts. This paper describes our work toward a novel
multiple object tracking benchmark aimed to address such issues. We discuss the
challenges of creating such a framework, collecting existing and new data,
gathering state-of-the-art methods to be tested on the datasets, and finally
creating a unified evaluation system. With MOTChallenge we aim to pave the way
toward a unified evaluation framework for a more meaningful quantification of
multi-target tracking.

本文介绍了我们的工作，即创建一个新的多目标跟踪基准，以解决现有基准存在的问题，并通过 MOTChallenge 向一个更有意义的多目标跟踪定量评估统一框架迈进。