Matching People Across Surveillance Cameras

Abstract

This work addresses the person re-identification problem, which consists on matching images of individuals captured by multiple and non-overlapping surveillance cameras. Works from literature tackle this problem proposing robust feature descriptors and matching functions, where the latter is responsible to assign the correct identity for individuals and is the focus of this work. Specifically, we propose two matching methods: the Kernel MBPLS and the Kernel X-CRC. The Kernel MBPLS is a nonlinear regression model that is scalable with respect to the number of cameras and allows the inclusion of additional labelled information (e.g., attributes). Differently, the Kernel X-CRC is a nonlinear and multitask matching function that can be used jointly with subspace learning approaches to boost the matching rates. We present an extensive experimental evaluation of both approaches in four datasets (VIPeR, PRID450S, WARD and Market-1501). Experimental results demonstrate that the Kernel MBPLS and the Kernel X-CRC outperforms approaches from literature. Furthermore, we show that the Kernel X-CRC can be successfuly applied in large-scale and multiple cameras datasets.