Human Re-identification with Global and Local Siamese Convolution Neural Network

Abstract

Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply hand-crafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convolution Neural Network which automatically extracts features from input images to perform human re-identification task. Besides, most of the current human re-identification task in single-shot approaches do not consider occlusion issue due to lack of tracking information. Therefore, we apply a decision fusion technique to combine global and local features for occlusion cases in single-shot approaches.