Multi‐label based view learning for vehicle re‐identification

Abstract

Due to the high similarity of different vehicles with similar appearances and the great diversity of camera viewpoints, vehicle re-identification (ReID) is still a challenging task. It commonly maps query set image into a high dimensional embedding space and then retrieve gallery vehicle images according to the distance. In this letter, a multi-label based view learning (MLVL) model to enhance the distinguishability of intra-class and inter-class through multiple labels learning, and decrease the distance of intra-class features caused by multi-view appearances of vehicles. Specifically, the model includes two main parts. First, the vehicle orientation estimation module is responsible for detecting key-points of the vehicle and predicting its orientation, which provides the orientation label for the multi-label based learning network to enhance view-invariant representation. Second, a novel dual-triplet and quadruplet loss function is designed to optimise intra-class and inter-class distance with the help of multi-label information, i.e. vehicle colour, type and orientation. In particular, a multi-label based sampler is proposed to generate training mini-batches instead of random sampler. Extensive experiments results show that the proposed MLVL model achieves 5.6 % $\%$ mAP improvement on VeRi-776 and 1.9 % $\%$ on VERI-Wild datasets compared with baseline model.