An Improved Dynamic Alignment Method for Vehicle ReIdentification * In this paper, we propose an improved vehicle reidentification method based on the combination between the AlignedRelD and the Stochastic Weight Averaging (SWA). AlignedRelD extracts a global feature and local features of a vehiclexe2x80x99s image and performs joint learning. Local automatic alignment is achieved by computing the shortest path between the two sets of local features, so that global feature learning can benefit from local feature learning. By running an optimizer with a high constant learning rate, the SWA averages the weight of the model to ensure that a better weight combination can be found. Our method achieves rank1 accuracy of 94.4% on VeRi776 and 95.1% on VehiclelD(small), outperforming stateoftheart methods by a large margin. In order to better solve the task of vehicle reidentification in residential area, we have made the OeasyParking dataset and experimented with our methods, and achieved good results.

Global and Local Deep Feature Representation Fusion for Vehicle ReIdentification This paper introduces our submission to the Grand Challenges on Vehicle ReIdentification (ReID) held in the VCIP 2019. Vehicle ReIdentification, which aims to retrieve images of a query vehicle from a largescale vehicle database, is of great significance to the urban security and city management. Although significant progress has been made in the last decade, vehicle ReID in the wild remains a very challenging problem due to the large intraclass variations of one vehicle instance from viewpoint, illumination, occlusion patterns, and the possibly small interclass differentiation of different vehicle instances (e.g., two different vehicles of the same model and color). To deal with these challenges, we in this work present a vehicle ReID framework that integrates both the global visual cues along with the local partbased cues to learn discriminative feature representations. In addition, the proposed framework also makes use of the extra information such as brands, models and colors to further improve the performance. Experimental results are performed on the VCIP 2019 VehicleReID dataset and the proposed framework achieved the second place in the competition.