No Identity, no problem: Motion through detection for people tracking

TL;DR

This paper introduces a novel motion-based tracking method that leverages detection heatmaps and motion estimates without requiring identity annotations, improving robustness in crowded and low frame-rate scenarios.

Contribution

The proposed approach predicts heatmaps and motion between frames, enforcing consistency without needing identity labels, advancing tracking accuracy in challenging conditions.

Findings

Achieves state-of-the-art results on MOT17 and WILDTRACK datasets.

Effective in crowded scenes and low frame-rate sequences.

Eliminates the need for costly identity annotations during training.

Abstract

Tracking-by-detection has become the de facto standard approach to people tracking. To increase robustness, some approaches incorporate re-identification using appearance models and regressing motion offset, which requires costly identity annotations. In this paper, we propose exploiting motion clues while providing supervision only for the detections, which is much easier to do. Our algorithm predicts detection heatmaps at two different times, along with a 2D motion estimate between the two images. It then warps one heatmap using the motion estimate and enforces consistency with the other one. This provides the required supervisory signal on the motion without the need for any motion annotations. In this manner, we couple the information obtained from different images during training and increase accuracy, especially in crowded scenes and when using low frame-rate sequences. We show that our approach delivers state-of-the-art results for single- and multi-view multi-target tracking on the MOT17 and WILDTRACK datasets.