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.
Note to Practitioners
—This work is motivated by the need to quickly recognize a target person captured across multiple camera views in a crowded environment. Presently, there is no ideal person re-identification solution. This article highlights the future requirements of smart visual surveillance through person detection and re-identification (re-id). The live feed of CCTV cameras can simultaneously detect and re-identify the target person to proactively handle the surveillance issues. The method detects and identifies a person’s identity captured in a CCTV by searching across an extensive database of images known as a gallery set. However, it is difficult to automatically recognize an individual across multiple camera views due to challenging scenarios such as low resolution, occlusion, background clutter, viewpoint, and illumination variations. Thanks to the deep learning-based body-part partition strategies that facilitate learning discriminative features of the target person. Traditionally, hard and soft partition strategies were used to partition the body parts. However, the proposed method focuses on a recent convolutional part partition strategy. Most part-based approaches assume that all the pixels in each part partition are homogeneous. However, the proposed method highlights the within-part-inconsistency problem. Against this background, this paper provides researchers and practitioners with a short review of the part-based body partition strategies. The proposed method demonstrates the effectiveness of convolutional part-partition over the hard and soft partition of body parts over three publicly available benchmark datasets. The proposed process also introduces a refine strategy to reduce the within-part-inconsistency issues in the part partitions.