Vehicle ReIdentification: Logistic Triplet Embedding Regularized by Label Smoothing The explosive increasing of vehicles cause amount of traffic problems. Although vehicle reidentification (ReID) can help to acquire and manage vehicles, some intrinsic difficulties hinder the application of vehicle ReID. For example, vehicles have little interinstance discrepancy due to their rigid structures and finite models. To address this problem, in this paper, a logistic triplet loss is proposed to fuse a labelsmoothing cross entropy to extract finegrained feature embeddings. Via exploring deeper into the interinstance variances, the novel loss combines advantages of classification and metric learning, and reveals more stable performance than popular triplet loss. The experimental results on public datasets demonstrate the effectiveness of the proposed loss compared with stateoftheart approaches.

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.