A New Path-Oriented Optimization Procedure for Consistent Person Reidentification in a Camera Network

Common person re-identification (re-ID) approaches focus on finding person signature similarities extracted in pairs of cameras without explicitly maintaining consistency of the re-ID results across the camera network. In contrast, the so-called NCR [2] approach considers all person detections in the networked cameras as a weighted graph, and then partitions the graph into clusters aiming at maximizing the sum of the cluster profits. NCR clustering uses a parameter K ∈ [0, 1] to offset similarity scores, making them negative. The partitioning is highly sensitive to the tuning of this value, which significantly impacts it. To counter this drawback, a new D(Directed)-NCR approach is proposed in this paper. It exploits a directed graph structure that explicitly incorporates temporal and topological information, and also employs a clustering approach to estimate the number of paths associated with different identities in the network. Evaluations on the well-known public datasets HDA and RPIfield demonstrate that D-NCR outperforms the NCR approach.