Open Access

ARTICLE

Vehicle Re-Identification Model Based on Optimized DenseNet121 with Joint Loss

Xiaorui Zhang^1,2,*, Xuan Chen¹, Wei Sun², Xiaozheng He³

1 Jiangsu Engineering Center of Network Monitoring, Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3 Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

* Corresponding Author: Xiaorui Zhang. Email:

Computers, Materials & Continua 2021, 67(3), 3933-3948. https://doi.org/10.32604/cmc.2021.016560

Received 21 December 2020; Accepted 02 February 2021; Issue published 01 March 2021

Abstract

With the increasing application of surveillance cameras, vehicle re-identification (Re-ID) has attracted more attention in the field of public security. Vehicle Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar appearances. Plentiful existing methods focus on local attributes by marking local locations. However, these methods require additional annotations, resulting in complex algorithms and insufferable computation time. To cope with these challenges, this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint loss. This model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it, then features are transferred to the deep layer by adjusting the corresponding weights, which reduces the transmission of redundant information in the process of feature reuse in DenseNet121. At the same time, the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ability. Additionally, a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difficult-to-separate samples by enlarging the weight of the difficult-to-separate samples during the training process. Experimental results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5% and 94.8%, respectively. Besides, Rank-1 on small, medium and large sub-datasets of Vehicle ID dataset reach 81.3%, 78.9%, and 76.5%, respectively, which surpasses most existing vehicle Re-ID methods.

---

Keywords

---