Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification

• URL: http://arxiv.org/abs/2003.07139v1
• Date: Mon, 16 Mar 2020 12:15:31 GMT
• Title: Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification
• Authors: Huibing Wang, Jinjia Peng, Guangqi Jiang, Fengqiang Xu, Xianping Fu
• Abstract summary: Vehicle re-identification (re-ID) technology is a challenging task since vehicles of the same design or manufacturer show similar appearance. We propose Triplet Center Loss based Part-aware Model ( TCPM) that leverages the discriminative features in part details of vehicles to refine the accuracy of vehicle re-identification. Our proposed TCPM has an enormous preference over the existing state-of-the-art methods on benchmark datasets VehicleID and VeRi-776.
• Score: 13.556590446194283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of smart cities, urban surveillance video analysis will play a further significant role in intelligent transportation systems. Identifying the same target vehicle in large datasets from non-overlapping cameras should be highlighted, which has grown into a hot topic in promoting intelligent transportation systems. However, vehicle re-identification (re-ID) technology is a challenging task since vehicles of the same design or manufacturer show similar appearance. To fill these gaps, we tackle this challenge by proposing Triplet Center Loss based Part-aware Model (TCPM) that leverages the discriminative features in part details of vehicles to refine the accuracy of vehicle re-identification. TCPM base on part discovery is that partitions the vehicle from horizontal and vertical directions to strengthen the details of the vehicle and reinforce the internal consistency of the parts. In addition, to eliminate intra-class differences in local regions of the vehicle, we propose external memory modules to emphasize the consistency of each part to learn the discriminating features, which forms a global dictionary over all categories in dataset. In TCPM, triplet-center loss is introduced to ensure each part of vehicle features extracted has intra-class consistency and inter-class separability. Experimental results show that our proposed TCPM has an enormous preference over the existing state-of-the-art methods on benchmark datasets VehicleID and VeRi-776.

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