Parsing-based View-aware Embedding Network for Vehicle Re-Identification

• URL: http://arxiv.org/abs/2004.05021v1
• Date: Fri, 10 Apr 2020 13:06:09 GMT
• Title: Parsing-based View-aware Embedding Network for Vehicle Re-Identification
• Authors: Dechao Meng and Liang Li and Xuejing Liu and Yadong Li and Shijie Yang and Zhengjun Zha and Xingyu Gao and Shuhui Wang and Qingming Huang
• Abstract summary: We propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID. The experiments conducted on three datasets show that our model outperforms state-of-the-art methods by a large margin.
• Score: 138.11983486734576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance distance caused by different views and the subtle inter-instance discrepancy caused by similar vehicles. In this paper, we propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID. First, we introduce a parsing network to parse a vehicle into four different views, and then align the features by mask average pooling. Such alignment provides a fine-grained representation of the vehicle. Second, in order to enhance the view-aware features, we design a common-visible attention to focus on the common visible views, which not only shortens the distance among intra-instances, but also enlarges the discrepancy of inter-instances. The PVEN helps capture the stable discriminative information of vehicle under different views. The experiments conducted on three datasets show that our model outperforms state-of-the-art methods by a large margin.

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