DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification

• URL: http://arxiv.org/abs/2003.11315v2
• Date: Sat, 28 Mar 2020 04:34:22 GMT
• Title: DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification
• Authors: Rixing Zhu, Jianwu Fang, Hongke Xu, Hongkai Yu, Jianru Xue
• Abstract summary: This paper formulates a multi-order deep cross-distance learning model for vehicle re-identification. One-view CycleGAN model is developed to alleviate exhaustive and enumerative cross-camera matching problem. Experiments on three vehicle Re-ID datasets demonstrate that the proposed method achieves significant improvement over the state-of-the-arts.
• Score: 22.547915009758256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle re-identification (Re-ID) has become a popular research topic owing to its practicability in intelligent transportation systems. Vehicle Re-ID suffers the numerous challenges caused by drastic variation in illumination, occlusions, background, resolutions, viewing angles, and so on. To address it, this paper formulates a multi-order deep cross-distance learning (\textbf{DCDLearn}) model for vehicle re-identification, where an efficient one-view CycleGAN model is developed to alleviate exhaustive and enumerative cross-camera matching problem in previous works and smooth the domain discrepancy of cross cameras. Specially, we treat the transferred images and the reconstructed images generated by one-view CycleGAN as multi-order augmented data for deep cross-distance learning, where the cross distances of multi-order image set with distinct identities are learned by optimizing an objective function with multi-order augmented triplet loss and center loss to achieve the camera-invariance and identity-consistency. Extensive experiments on three vehicle Re-ID datasets demonstrate that the proposed method achieves significant improvement over the state-of-the-arts, especially for the small scale dataset.

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