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Orientation-Aware Vehicle Re-Identification with Semantics-Guided Part Attention Network

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12347)

Abstract

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention mechanisms to help vehicle re-ID, they often require expensive keypoint labels or suffer from noisy attention mask if not trained with expensive labels. In this work, we propose a dedicated Semantics-guided Part Attention Network (SPAN) to robustly predict part attention masks for different views of vehicles given only image-level semantic labels during training. With the help of part attention masks, we can extract discriminative features in each part separately. Then we introduce Co-occurrence Part-attentive Distance Metric (CPDM) which places greater emphasis on co-occurrence vehicle parts when evaluating the feature distance of two images. Extensive experiments validate the effectiveness of the proposed method and show that our framework outperforms the state-of-the-art approaches.

Keywords

Vehicle re-identification Spatial attention Semantics-guided learning Visibility-aware features 

Notes

Acknowledgment

This research was supported in part by the Ministry of Science and Technology of Taiwan (MOST 108-2633-E-002-001), National Taiwan University (NTU-108L104039), Intel Corporation, Delta Electronics and Compal Electronics.

Supplementary material

504434_1_En_20_MOESM1_ESM.pdf (8.3 mb)
Supplementary material 1 (pdf 8525 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Graduate Institute of Electronic EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.NTU IoX CenterNational Taiwan UniversityTaipeiTaiwan

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