Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification

Authors

  • Haiyun Guo University of Chinese Academy of Sciences, Institute of Automation
  • Chaoyang Zhao University of Chinese Academy of Sciences, Institute of Automation
  • Zhiwei Liu University of Chinese Academy of Sciences, Institute of Automation
  • Jinqiao Wang University of Chinese Academy of Sciences, Institute of Automation
  • Hanqing Lu University of Chinese Academy of Sciences, Institute of Automation

Keywords:

Vehicle re-identification, CNN, ranking loss

Abstract

Vehicle re-identification (re-ID) is to identify the same vehicle across different cameras. It’s a significant but challenging topic, which has received little attention due to the complex intra-class and inter-class variation of vehicle images and the lack of large-scale vehicle re-ID dataset. Previous methods focus on pulling images from different vehicles apart but neglect the discrimination between vehicles from different vehicle models, which is actually quite important to obtain a correct ranking order for vehicle re-ID. In this paper, we learn a structured feature embedding for vehicle re-ID with a novel coarse-to-fine ranking loss to pull images of the same vehicle as close as possible and achieve discrimination between images from different vehicles as well as vehicles from different vehicle models. In the learnt feature space, both intra-class compactness and inter-class distinction are well guaranteed and the Euclidean distance between features directly reflects the semantic similarity of vehicle images. Furthermore, we build so far the largest vehicle re-ID dataset "Vehicle-1M," which involves nearly 1 million images captured in various surveillance scenarios. Experimental results on "Vehicle-1M" and "VehicleID" demonstrate the superiority of our proposed approach.

Downloads

Published

2018-04-27

How to Cite

Guo, H., Zhao, C., Liu, Z., Wang, J., & Lu, H. (2018). Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12237