Poses Guide Spatiotemporal Model for Vehicle Re-identification

Abstract

In this paper, we tackle the vehicle Re-identification (Re-ID) problem, which is important in the urban surveillance. Utilizing visual appearance information is limited on performance due to occlusions, illumination variations, etc. To make the best of our knowledge, the recent few methods consider the spatiotemporal information to solve vehicle Re-ID problem, and neglect the influence of driving direction. In this paper, we explore that the spatiotemporal distribution of vehicle movements follows certain rules, moreover the vehicles’ poses on camera view indicate their directions are closely related to the spatiotemporal cues. Inspired by these two observations, we propose a vehicles’ Poses Guide Spatiotemporal model (PGST) for assisting vehicle Re-ID. Firstly, a Gaussian distribution based spatiotemporal probability model is exploited to predict the vehicle’s spatiotemporal movement. Then a CNN embedding poses classifier is exploited to estimate driving direction by evaluating vehicle’s pose. Finally, PGST model is integrated into the framework which fuses the results of visual appearance model and spatiotemporal model together. Due to the lack of vehicle dataset with spatiotemporal information and topology of cameras, experiments are conducted on a public vehicle Re-ID dataset which is the only one meeting the experiments requirements. The proposed approach achieves competitive performances.