Multi-Depth Deep Similarity Learning for Person Re-Identification

Abstract

Detecting same people in different surveillance cameras, named person re-identification, has become a challenging and critical task in image processing. Since surveillance images usually have low resolution and different viewpoints, matching persons on them is still difficult. In this paper, a proposed method for person re-identification is introduced based on exploring similarity in different depth layers of convolutional neural network (CNN). To this end, after determining each person as a category for training CNN, optimum filters are obtained to find the best discriminative feature maps based on them. Smoothed discriminative features (SDF) are defined to compute similarity between persons. Experimental results, performed on CUHK01 database, demonstrate that the proposed method outperforms state-of-the-art feature extraction methods for person re-identification.