CVPR19 Tracking and Detection Challenge: How crowded can it get?

TL;DR

This paper introduces a new benchmark dataset for multiple object tracking in highly crowded scenes, aiming to evaluate and improve tracking methods under extreme crowding conditions.

Contribution

It presents a new CVPR19 benchmark with 8 challenging crowded sequences to assess the performance of multi-object tracking algorithms.

Findings

Benchmark highlights challenges in crowded scenes

Provides a standardized evaluation framework

Encourages development of robust tracking methods

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 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 CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios.