Stronger Baseline for Vehicle ReIdentification in the Wild Recently, reidentification tasks in computer vision field draw attention. Vehicle reidentification can be used to find the suspect car (target) from a vast surveillance video dataset. One of the most critical issues in the vehicle reidentification task is how to learn the effective feature representation. In general, pairwise learning such as the contrastive and triplet loss functions is adopted to learn the discriminative feature based on the convolution neural network. A good backbone network will lead to a significant improvement in the car reidentification task. In this paper, a stronger baseline method is proposed to achieve a better feature representation ability. First, we integrate the shiftinvariant convolutional neural network with ResNet backbone to enhance the consistency feature learning. Afterward, a multilayer feature fusion module is proposed to incorporate the middle and highlevel features to further improve the performance of car reidentification. Experimental results demonstrated that the proposed stronger baseline method achieves stateoftheart performance in terms of mean averaging precision.

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.