An effective implementation of Gaussian of Gaussian descriptor for person re-identification

Person re-identification (ReID), a critical task in surveillance systems, has obtained impressive advances in recent years. However, most current works focus on improving the person re-identification accuracy. In practical terms, the direct use of these works seems difficult, even infeasible. Among the features proposed for person representation in person ReID, Gaussian of Gaussian (GOG) has been proved to be robust. Towards applying this feature for practical usage, in this work, we simultaneously propose two improvements. First, we re-implement and perform intensive experiments to select the optimal parameters of GOG during feature extraction. Second, we propose and apply preprocessing techniques on person images. The experimental results show that the proposed approach allows to extract GOG 2 times faster than the available source code and achieve remarkably high accuracy for person ReID. The obtained accuracies at rank-1 on VIPeR dataset are 51.74% (with background) and 57.25% (without background). The implementations and evaluation datasets used in this paper are made publicly available.