Multi‐label based view learning for vehicle re‐identification
Yichu Liu, Haifeng Hu, Dihu Chen · Electronics Letters · 2021
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.