Multi-Label-Based Similarity Learning for Vehicle Re-Identification

Abstract

The massive attention to the surveillance video-based analysis makes the vehicle re-identification one of the current hot areas of interest to study. Extracting discriminative visual representations for vehicle re-identification is a challenging task due to the low-variance among the vehicles that share same model, brand, type, and color. Recently, several methods have been proposed for vehicle re-identification, that either use feature learning or metric learning approach. However, designing an efficient and cost-effective model is significantly demanded. In this paper, we propose multi-label-based similarity learning (MLSL) for vehicle re-identification obtaining an efficient deep-learning-based model that derives robust vehicle representations. Overall, our model features two main parts. First, a multi-label-based similarity learner that employs Siamese network on three different attributes of the vehicles: vehicle ID, color, and type. The second part is a regular CNN-based feature learner that employed to learn feature representations with vehicle ID attribute. The model is trained jointly with both parts. In order to validate the effectiveness of our model, a set of extensive experiments has been conducted on three of the largest well-known datasets VeRi-776, VehicleID, and VERI-Wild datasets. Furthermore, the parts of the proposed model are validated by exploring the influence of each part on the entire model performance. The results prove the superiority of our model over multiple state-of-the-art methods on the three mentioned datasets.