Consensus-based Inter-camera Re-identification - Across Non-overlapping Views

Multi-object re-identification across cameras network with non-overlapping fields of view is a challenging problem. Firstly, the visual signature of the same object might be very different from one camera to another. Secondly, the blind zone between cameras creates the discontinuity in the observation of the same object in terms of locations and travelling times. Centralized inferences proposed in literature for inter-camera re-identification becomes insufficient in practice mostly with the requirement of real-time applications and dynamic cameras network. In this paper we present a completely distributed approach for inter-camera reidentification. The proposed approach based on the distributed inferences, where the set of smart-cameras collaborate to reach a consensus about the identities of objects circulating in the network. Local and global visual descriptors were combined into the proposed approach for inter-camera color mapping and invariant objects description. Experimental results of applying this approach show improvement in inter-camera reidentification and robustness in recovering from very complex conditions.