Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views

TL;DR

This paper introduces a real-time, multi-view 3D human pose tracking method that uses greedy bipartite matching to efficiently associate multiple people across frames, improving accuracy and resolving occlusions.

Contribution

It presents a novel greedy matching approach for multi-view 3D human pose estimation that is efficient, accurate, and serves as a strong baseline for future research.

Findings

Achieves state-of-the-art results on benchmark datasets.

Effectively resolves occlusions and ambiguities in multi-view tracking.

Operates in real-time with high accuracy.

Abstract

In this work we propose an approach for estimating 3D human poses of multiple people from a set of calibrated cameras. Estimating 3D human poses from multiple views has several compelling properties: human poses are estimated within a global coordinate space and multiple cameras provide an extended field of view which helps in resolving ambiguities, occlusions and motion blur. Our approach builds upon a real-time 2D multi-person pose estimation system and greedily solves the association problem between multiple views. We utilize bipartite matching to track multiple people over multiple frames. This proofs to be especially efficient as problems associated with greedy matching such as occlusion can be easily resolved in 3D. Our approach achieves state-of-the-art results on popular benchmarks and may serve as a baseline for future work.