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

arXiv: https://arxiv.org/abs/2003.11315 · HTML: https://mlsanity.com/paper/2003.11315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification](https://mlsanity.com/paper/2302.05673/) (2023)

## Abstract (arXiv)

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 (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.