论文标题
MOT20：在拥挤场景中进行多对象跟踪的基准
MOT20: A benchmark for multi object tracking in crowded scenes
论文作者
论文摘要
标准化基准对于大多数计算机视觉应用至关重要。尽管排行榜和排名表不应过分掌握，但基准通常提供最客观的绩效衡量标准，因此是研究的重要指南。启动了多个对象跟踪的基准Motchallenge，其目标是建立对多个对象跟踪方法的标准化评估。挑战集中在多个人的跟踪上，因为在跟踪社区中对行人进行了很好的研究，并且精确的跟踪和检测具有很高的实践意义。自第一个版本以来，MOT15，MOT16和MOT17通过引入干净的数据集和精确的框架为基准的多对象跟踪器引入了巨大的贡献。在本文中，我们介绍了Mot20Benchmark，其中包括8个新序列，描绘了非常拥挤的挑战场景。该基准首先在2019年计算机视觉和模式识别会议（CVPR）的第4个BMTT MOT挑战研讨会上提出，并有机会评估最先进的方法，用于处理极度拥挤的场景时多个对象跟踪。
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 research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16, and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our MOT20benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark was presented first at the 4thBMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for multiple object tracking when handling extremely crowded scenarios.
