MOT16: A Benchmark for Multi-Object Tracking

TLDR

Standardized benchmarks are essential in computer vision, offering objective performance measures that guide research. This paper presents the new MOT16 release of the MOTChallenge benchmark. MOT16 expands the dataset to include multiple object classes beyond pedestrians, with consistent annotations and visibility levels for each object. The release markedly increases labeled boxes and introduces visibility annotations for every tracked object.

Abstract

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. This paper accompanies a new release of the MOTChallenge benchmark. Unlike the initial release, all videos of MOT16 have been carefully annotated following a consistent protocol. Moreover, it not only offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest.