Orientation-Discriminative Feature Representation for Decentralized Pedestrian Tracking
This work addresses the problem of limited communication bandwidth for robotic applications by enabling decentralized tracking, though it appears incremental as it builds on existing tracking frameworks.
The paper tackles decentralized pedestrian tracking in sensor networks by proposing a communication-efficient, orientation-discriminative feature representation and a cross-sensor track association approach, resulting in improved performance on public datasets.
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.