Vehicle Re-identification Using the Coupled Feature Clusters Embedded into Triplet Loss

Vehicle re-identification is the task of identifying the same vehicle across some images captured by multiple cameras. We propose a coupled feature clusters embedded into triplet loss dealing with hard samples. During the vehicle re-identification, the coupled clusters loss suffers from larger computation consumption caused by the extension of the sample scale and the reduction of identification accuracy. Therefore, the coupled feature clusters embedded into triplet loss is proposed. It improves the ability of the algorithm on processing hard samples in terms of selecting feature centers of positive samples based on clustering and the embedded into a triple loss. Experiments show that the algorithm effectively improves the accuracy of vehicle re-identification compared to the vehicle re-identification algorithm based on coupled clusters loss. © 2020, Chinese Institute of Electronics. All right reserved.