Person re-identification using region covariance in a multi-feature approach

Abstract

Person re-identification is an important requirement for modern video surveillance systems and relevant for human tracking, especially over camera networks. Many different approaches have been proposed but a robust identification under real-life conditions still remains hard. In this paper we investigate the fusion of multiple person descriptors in order to increase the performance using complementary feature vectors. As an additional improvement to state-of-the-art region covariance descriptors, an extension of the comparison metric is proposed which increases the robustness and performance of the system in cases of rank deficiency. The proposed system is evaluated on the well-known benchmarks CAVIAR4REID, VIPeR, ETHZ and PRID 2011 and shows significant improvements over existing re-identification algorithms.