A Pedestrian Re-identification Method Based on Multi-frame Fusion Part-based Convolutional Baseline Network

Abstract

In recent years, with the increasingly perfect monitoring system, how to make full use of the existing monitoring system to do security work has become a concern in the security field. Face recognition can be used in the security field, but it is difficult to play a role in the surveillance field because it usually requires the cooperation of pedestrians. Therefore, the pedestrian recognition technology without the cooperation of pedestrians has been widely concerned. In this paper, in order to realize a given sequence of monitoring pedestrian images and retrieve pedestrian images across devices, we proposed a new method to realize high- precision pedestrian recognition. First, because surveillance video is a series of pedestrian sequences, we proposed a Crossover Filtering Module (CFM) to screen video sequences for key frames. Then, we propose a network named Multi-frame Fusion Part- based Convolutional Baseline (MFPCB) to extract the features of screened keyframes. Finally, we use the cosine distance to measure the features and find the pedestrian image across the device. This paper mainly studies feature comparison and extraction, which can solve the problems of pedestrian occlusion and location under different cameras. Experiment confirms that MFPCB allows pedestrian recognition to gain another round of performance boost. For instance, on the Mars dataset, we achieve 77.3% mAP and 88.6% rank-1 accuracy, surpassing the state of the art by a large margin.