Vehicle Matching Based on Similarity Metric Learning

Abstract

With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems. Recent methods explored the vehicle matching based on the feature extraction. Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching. But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times. To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity metric from both domains (i.e., Day pattern or Night pattern) for vehicle matching. To exploit properties and relationships among vehicle datasets, we first apply the domain transformer to translate the domain of vehicle images, and then utilize the two-channel Siamese network to extract features from both domains for better feature similarity learning. Experimental results illustrate that our models achieve improvements over state-of-the-arts.