Orientation-Discriminative Feature Representation for Decentralized Pedestrian Tracking

TL;DR

This paper introduces a decentralized pedestrian tracking method using an orientation-discriminative feature representation that improves multi-sensor tracking performance while reducing communication bandwidth in sensor networks.

Contribution

It proposes a novel, communication-efficient feature representation and a cross-sensor track association approach for decentralized pedestrian tracking.

Findings

Improved multi-sensor tracking accuracy on public datasets.

Reduced communication bandwidth compared to centralized methods.

Effective in real-world robotic sensor networks.

Abstract

This paper focuses on the problem of decentralized pedestrian tracking using a sensor network. Traditional works on pedestrian tracking usually use a centralized framework, which becomes less practical for robotic applications due to limited communication bandwidth. Our paper proposes a communication-efficient, orientation-discriminative feature representation to characterize pedestrian appearance information, that can be shared among sensors. Building upon that representation, our work develops a cross-sensor track association approach to achieve decentralized tracking. Extensive evaluations are conducted on publicly available datasets and results show that our proposed approach leads to improved performance in multi-sensor tracking.