Stronger Baseline for Vehicle Re-Identification in the Wild

Abstract

Recently, re-identification tasks in computer vision field draw attention. Vehicle re-identification can be used to find the suspect car (target) from a vast surveillance video dataset. One of the most critical issues in the vehicle re-identification 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 re-identification task. In this paper, a stronger baseline method is proposed to achieve a better feature representation ability. First, we integrate the shift-invariant convolutional neural network with ResNet backbone to enhance the consistency feature learning. Afterward, a multi-layer feature fusion module is proposed to incorporate the middle- and high-level features to further improve the performance of car re-identification. Experimental results demonstrated that the proposed stronger baseline method achieves state-of-the-art performance in terms of mean averaging precision.