Pedestrian Re-Identification Algorithm Based on Multivariate Manifold Metric-Anti-Noise Manifold Space Learning

Abstract

Deep learning is widely used in pedestrian re-identification, but pedestrian images are susceptible to changes in illumination, environmental noise, and other factors. These factors have affected the in-depth promotion of this type of method. First, the existing models cannot effectively extract the structural information of low-light pedestrian samples. Second, environmental noise information will cause the model to fail to extract stable and reliable feature information. In this paper, to solve the problems as mentioned above, we propose a multi-group manifold metric learning model. Our method can obtain manifold conditions under different lighting conditions. It can retain the inherent structure of low-dimensional spatial data in high-dimensional space, and realize the extraction of features of pedestrians in low-light conditions. In addition, we proposed an anti-noise manifold space learning method and designed a manifold module network structure. The manifold modules are stacked layer by layer to construct a manifold convolutional neural network further. Our method can extract robust pedestrian image features and reduce noise disturbance to the model. We use noise and low-light images as the main experimental objects. The experimental results show that our proposed method can achieve the most advanced performance in multiple ReID data sets.