Zheng, Aihua, Zhu, Xianpeng, Ma, Zhiqi, Li, Chenglong ORCID: https://orcid.org/0000-0002-7233-2739, Tang, Jin and Ma, Jixin (2023) Cross-directional consistency network with adaptive layer normalization for multi-spectral vehicle re-identification and a high-quality benchmark. Information Fusion, 100:101901. pp. 1-16. ISSN 1566-2535 (Print), 1872-6305 (Online) (doi:10.1016/j.inffus.2023.101901)

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Abstract

To tackle the challenge of vehicle re-identification (Re-ID) in complex lighting environments and diverse scenes, multi-spectral sources like visible and infrared information are taken into consideration due to their excellent complementary advantages. However, multi-spectral vehicle Re-ID suffers cross-modality discrepancy caused by heterogeneous properties of different modalities as well as a big challenge of the diverse appearance with different views in each identity. Meanwhile, diverse environmental interference leads to heavy sample distributional discrepancy in each modality. In this work, we propose a novel cross-directional consistency network (CCNet) to simultaneously overcome the discrepancies from both modality and sample aspects. In particular, we design a new cross-directional center loss (
) to pull the modality centers of each identity close to mitigate cross-modality discrepancy, while the sample centers of each identity close to alleviate the sample discrepancy. Such a strategy can generate discriminative multi-spectral feature representations for vehicle Re-ID. In addition, we design an adaptive layer normalization unit (ALNU) to dynamically adjust individual feature distribution to handle distributional discrepancy of intra-modality features for robust learning. To provide a comprehensive evaluation platform, we create a high-quality RGB-NIR-TIR multi-spectral vehicle Re-ID benchmark (MSVR310), including 310 different vehicles from a broad range of viewpoints, time spans and environmental complexities. Comprehensive experiments on both created and public datasets demonstrate the effectiveness of the proposed approach comparing to the state-of-the-art methods. The dataset and code will be released for free academic usage at https://github.com/superlollipop123/Cross-directional-Center-Network-and-MSVR310.

Item Type:	Article
Uncontrolled Keywords:	Vehicle Re-ID; multi-spectral representation; cross-directional center consistency; layer normalization; benchmark dataset
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	https://www.sciencedirect.com/science/ar...
Last Modified:	13 Jul 2023 08:57
URI:	http://gala.gre.ac.uk/id/eprint/43169

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