Large-Scale Person Re-Identification for Crowd Monitoring in Emergency

The task of associating photographs/videos of an individual obtained from the same camera on various occasions or across cameras is called Person Re-identification (PRId). Computer-aided monitoring of persons of interest is an active research area in automated visual surveillance. It becomes more substantial in emergencies like natural disasters, contrived incidents, and public health crises. Part-level features of a pedestrian image hold significant importance in person retrieval. Traditionally, part-based PRId tasks required pose estimators or body part detectors for the hard partition of the pedestrian image. However, such approaches attract additional issues due to their dependency on external cues. This article emphasized employing the convolutional partition of body parts to learn discriminative part features. We focus on two significant contributions: (I) A parallel architecture called Convolutional Part Refine (CPR) and (II) Three different convolutional part refine strategies of outliers to handle the existing inconsistencies of uniform partition. The experiments confirm that CPR achieves competitive performance with state-of-the-art methods.