  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.
