Vehicle re-identification by multi-grain learning

Vehicle re-identification (re-ID) is to identify the same vehicle captured by different cameras with non-overlapping views, which plays an important role in intelligent transportation system and traffic safety. Compared with face recognition and person re-ID tasks, it is difficult to train an effective vehicle re-ID model since different vehicles of the same vehicle model, such as Mercedes-Benz C300, may exhibit strong interclass similarity. To handle this difficulty, we propose a multi-grain ranking loss with the auxiliary of vehicle model, which models the vehicle re-ID task as two sub-tasks with different granularities including matching vehicles in the same vehicle model and different vehicle models. In particular, we infer the vehicle model labels online for the unlabeled training samples by clustering. The experimental results on two benchmarks demonstrate the proposed method can achieve state-of-the-art performance.