Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network

2020/08/26 by Tsai-Shien Chen, Chen, Tsai-Shien, Chih‐Ting Liu +5
Computer Science · Engineering · #Advanced Neural Network Applications #Video Surveillance and Tracking Methods #Vehicle License Plate Recognition

paper · pdf · doi:10.48550/arxiv.2008.11423

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.

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