Combining Pose Invariant and Discriminative Features for Vehicle Reidentification

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Abstract
Vehicle re-identification, aiming to identify vehicles across images, has drawn a lot attention and has made significant achievements recent years. However, vehicle re-identification still remains a challenging task because of severe appearance changes of a vehicle due to different orientations. In practice, the result of re-identification is greatly influenced by the pose of vehicles and we call this influence as “pose-barrier”. In this paper, we propose to use pose-invariant features and pose-discriminative features to address the pose-barrier. Pose-invariant features are expert in exploring the overall characteristic of the appearance. Meanwhile, pose-discriminative features are prior to distinguish the difference between local details. The framework of our method can be divided into three steps: orientation estimation, pose-invariant feature learning and pose discriminative feature learning, so-called pose-based complementary features (PCF). The pose-discriminative feature is trained together with a orientation discriminator which tries to judge the orientation of input images. On the other hand, adversarial loss is applied in a pose to train a pose-discriminative feature which does not contain orientation information. By combining the two features, the method can alleviate the influence of pose-barrier. Extensive experiments on a series of challenging databases demonstrate that the proposed framework outperforms the state-of-the-art results on vehicle re-identification dataset: VeRi-776.
Funding Information
  • National Key Research and Development Program of China (2018YFB2100500)
  • National Natural Science Foundation of China (61861166002, 61872025, 61635002)
  • Science and Technology Development Fund, Macau SAR (0001/2018/AFJ)
  • Fundamental Research Funds for the Central Universities
  • Open Fund of the State Key Laboratory of Software Development Environment (SKLSDE2019ZX-04)

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