Crowdsourcing-Based Ranking Aggregation For Person Re-Identification

Person re-identification (re-ID) is widely applied in surveillance and criminal detection applications. The existing research focus on devising the stand-alone re-ID methods, ignoring their practical application in the multi-person collaboration scenario. To improve the search efficiency, a group of investigators are usually assigned the same task to re-identify a suspect from a shared gallery set. Due to their personalized viewpoints and search feedback operations, different investigators may obtain diverse search results of the same query target. In this case, merging different rankings and generating an improved result is of great importance. To this end, this paper proposes a crowdsourcing-based ranking aggregation to adaptively fuse multiple ranking lists for re-ID problem. The method estimates the reliability of individual investigators, with a specifically designed long tail distribution to fit the top ranking demand, and is feasible for human-machine interaction. Extensive experiments conducted on four datasets demonstrate the superiority of the proposed method.