Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

TL;DR

This paper introduces a comprehensive benchmark for multiple object tracking, analyzing nearly 50 state-of-the-art methods on over 11,000 frames to identify current trends, weaknesses, and future research directions.

Contribution

It provides an in-depth evaluation framework and analysis of existing trackers, highlighting areas for improvement in multiple object tracking research.

Findings

Current methods show varied performance across different scenarios.

Weaknesses identified in occlusion handling and long-term tracking.

Benchmark results serve as a reference for future research directions.

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. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.