Incremental Re-Identification by Cross-Direction and Cross-Ranking Adaption

Person re-identification is widely applied in video surveillance and criminal investigation applications. To achieve better performance, an additional re-ranking step is often exploited. Related methods attempt to optimize the result according to every single query independently. However, in a practical scene, as the investigation process goes on, the other queries, in particular, the gradually accumulated logs, can be used to guide or regularize the current query. In this paper, we propose to optimize the result according to not only the current query itself but also the other queries and historical logs. We respectively investigate the cross-direction and the cross-ranking constraints among different queries. Based on the investigations, we propose a reciprocal optimization method to refine multiple ranking lists reciprocally. Experiments on the VIPeR, new-protocol CUHK03, and Market-1501 datasets confirm the effectiveness of our method. In particular, on the Market-1501 dataset, with full utilization of the other queries, the method achieves an accuracy rate of 94.66% at rank-1 and a very high mAP of 75.12%, and significantly outperforms the state-of-the-art methods.