Spatial Pyramid-Based Statistical Features for Person Re-Identification: A Comprehensive Evaluation

Abstract

Person re-identification (Re-Id) across nonoverlapping camera views is one of challenging problems in surveillance video analysis. The difficulties in person Re-Id mainly come from the large appearance variations caused by camera view angle, human pose, illumination, and occlusion. Recently, extensive efforts have been cast into addressing this problem by developing invariant features or discriminative distance metrics. However, there is still a lack of systematic evaluations on the pipeline for feature extraction and combination. In this paper, we propose a spatial pyramid-based statistical feature extraction framework as a unified pipeline of feature extraction and combination for person Re-Id, and systematically evaluate the configuration details in feature extraction and the fusion strategies in feature combination. Extensive experiments on benchmark datasets demonstrate the critical components in feature extraction. Moreover, by combining multiple features, our proposed approach can yield state-of-the-art performance. It should be mentioned that our approach achieves rank 1 matching rate of 45.8% on dataset VIPeR and 61.5% on dataset CUHK01, respectively.