Discriminative Feature Learning with Co-Occurrence Attention Network for Vehicle ReID

Hao Sheng, Shuai Wang, Haobo Chen, Da Yang, Yang Huang, Jiahao Shen, Wei Ke

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Vehicle Re-Identification (ReID) aims to find images of the same vehicle from different videos. It remains a challenging task in the video analysis field due to the huge appearance discrepancy of the same vehicle in cross-view matching and the subtle difference of different similar vehicles in same-view matching. In this paper, we propose a Co-occurrence Attention Net (CAN) to deal with these two challenges. Specifically, CAN consists of two branches, a main branch and an aware branch. The main branch is in charge of extracting global features that are consistent in most views. This feature encodes holistic information such as color and pose, however, it can not handle cross/same-view hard cases, as shown in Fig.1. Therefore, the aware branch is designed to focus on the local details and viewpoint information, which can become an important complement for those hard cases. Considering that the positions of local areas such as wheels and logos change with the viewpoint, Aware Attention Module is introduced to find the hidden relationship among local areas and seamlessly combine the viewpoint information simultaneously. Then, CAN is trained by a partition-and-reunion-based loss, which can narrow the intra-class distance and increase the inter-class distance. Further, an adaptive co-occurrence view emphasize strategy is adopted to fully utilize the learned features. Experimental results on three widely used datasets including VeRi-776, VehicleID and VERI-Wild demonstrate the effectiveness of our method and competitive performance with other state-of-the-art methods.

Original languageEnglish
Pages (from-to)3510-3522
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume34
Issue number5
DOIs
Publication statusPublished - 1 May 2024

Keywords

  • Vehicle re-identification
  • co-occurrence attention
  • discriminative learning
  • image representation

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