Multi-view Tracking Using Weakly Supervised Human Motion Prediction

TL;DR

This paper proposes a multi-view people-tracking method that predicts human motion over time to improve occlusion handling, demonstrating superior performance on benchmark datasets.

Contribution

It introduces a novel approach that predicts human motion for multi-view tracking, enhancing temporal and cross-view consistency beyond traditional detection-based methods.

Findings

Outperforms state-of-the-art methods on PETS2009 dataset

Effective in handling occlusions in crowded scenes

Validated on WILDTRACK dataset

Abstract

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and infer people's presence in individual frames from these. This enables to enforce consistency both over time and across views of a single temporal frame. We validate our approach on the PETS2009 and WILDTRACK datasets and demonstrate that it outperforms state-of-the-art methods.