Camera network based person re-identification by leveraging spatial-temporal constraint and multiple cameras relations

Abstract

With the rapid development of multimedia technology and vast demand on video investigation, long-term cross-camera object tracking is increasingly important in the practical surveillance scene. Because the conventional Paired Cameras based Person Re-identification (PCPR) cannot fully satisfy the above requirement, a new framework named Camera Network based Person Re-identification (CNPR) is introduced. Two phenomena have been investigated and explored in this paper. First, the same person cannot simultaneously appear in two non-overlapping cameras. Second, the closer two cameras, the more relevant they are, in the sense that persons can transit between them with a high probability. Based on these two phenomena, a probabilistic method is proposed with reference to both visual difference and spatial-temporal constraint, to address the novel CNPR problem. (i) Spatial-temporal constraint is utilized as a filter to narrow the search space for the specific query object, and then the Weibull Distribution is exploited to formulate the spatialtemporal probability indicating the possibility of pedestrians walking to a certain camera at a certain time. (ii) Spatial-temporal probability and visual feature probability are collaborated to generate the ranking list. (iii) The multiple camera relations related to the transitions are explored to further optimize the obtained ranking list. Quantitative experiments conducted on TMin and CamNeT datasets have shown that the proposed method achieves a better performance to the novel CNPR problem.