Fusing multiple statistical features via explicit feature mapping for person re-identification

Abstract

Person re-identification (Re-ID) across non-overlapping camera views is one of the challenging problems in surveillance video analysis. In this paper, we propose to combine multiple statistical features via explicit kernel feature mapping, and learn a linear metric model by local fisher discriminant analysis (LFDA) for person Re-ID. To strengthen the robustness of our representation, three complementary statistical characteristics, including histogram-like features, covariance matrix and expectation vector, were extracted from multiple spatial scales for each person image. Experimental results show that the proposed method works effectively on the popular benchmark data sets VIPeR and CUHK01 and yield impressive performance measured with Cumulative Match Characteristic curves (CMC).