Tracking-by-reidentification in a nonoverlapping camera network
This article tackles the problem of automatic multi-pedestrian tracking in non overlapping fields of view camera networks, using monocular, uncalibrated cameras. Tracking is locally addressed by a Tracking-by-Detection and reidentification algorithm. We propose here to introduce the concept of global identity into a multi-target tracking algorithm, qualifying people at the network level, to allow us to rebound observation discontinuities. We embed that identity into the tracking loop thanks to the mixed-state particle filter framework, thus including it in the search space. Doing so, each tracker maintains a mutli-modality on the identity in the network of its target. We increase the decision strength introducing a high level decision scheme which integrates all the trackers hypothesis over all the cameras of the network with previous reidentification results and the topology of the network. The tracking and reidentification module is first tested with a single camera. We then evaluate the whole framework on a 3 non-overlapping fields of views network with 7 identities. The only a priori knowledge assumed is a topological map of the network.
camera network particle filtering re-identification pedestrian tracking nonoverlapping fields of view