Group-sensitive triplet embedding for vehicle reidentification

The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation 'group' between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database 'PKU-Vehicle,' consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID.