Enhanced inter-camera person re-identification leveraging mixed-order relation-aware recurrent neural network

Abstract

The person Re-Identification (Re-ID) requires a significant quantity of the costly label information, whereas unsupervised ones are still unable to provide satisfactory identification performance. These results in the poor scalability due to the requirement of the laborious data collection and annotation process in real-world Re-id applications. Unsupervised Re-ID techniques not require identity label data, but have significantly worse and inadequate model performance. In this paper, Enhanced Inter-Camera Person Re-identification leveraging Mixed-Order Relation-Aware Recurrent Neural Network (EICPR-MORRNN-TTAO) is proposed. The input images are collected from Market-1501, MSMT17, and Duke MTMC-reID datasets. Afterward, the input image is supplied to pre-processing. In preprocessing, Unsharp Structure Guided Filtering (USGF) is employed to enhance image quality. The pre-processed image is supplied to classification phase for Re-identifying the Inter-Camera Person as Same and Different utilizing Mixed-Order Relation-Aware Recurrent Neural Network (MORRNN). Generally, MORRNN does not adopt any optimization methods to determine the ideal parameters to assure accurate person Re-identification. Hence, Triangulation Topology Aggregation Optimizer (TTAO) is proposed to enhance the weight parameters of MORRNN. The EICPR-MORRNN-TTAO method is implemented in Python. The metrics, like Mean Average Precision (MAP), Cumulative Matching Characteristic (CMC), recall, Rank-1, Rank-10, Rank-20, Entropy, error rate, and Receiver Operating Characteristic (ROC) is considered. The EICPR-MORRNN-TTAO method attains 23.10 %, 27.54 % and 25.72 %, higher mAP, 21.48 %, 17.73 %, 25.32 % higher CMC and 20.98 %, 26.66 % and 16.32 % lower Error rate, are compared with existing techniques, like Intra-camera supervised Re-ID (ICSP-RI-PR), Offline-online associated camera-aware proxies for unsupervised Re-ID(OSC-UPRI-EIC), and Unsupervised Re-ID with stochastic training strategy (UPRI-EIC-STS) respectively.