Abstract
Vehicle re-identification (Re-ID) technology plays an important role in intelligent video surveillance systems. Due to various factors, e.g., resolution variation, viewpoint variation, illumination changes, occlusion, etc., vehicle Re-ID is a very challenging computer vision task. In order to solve this problem, a joint pyramid feature representation network (JPFRN) is proposed in this paper. Based on the consideration that various convolution blocks with different depths hold various resolution and semantic information of the vehicle image, which can help to effectively identify the vehicle, the proposed JPFRN method obtains four vehicle feature blocks with different depths by designing pyramidal feature fusion of each convolution block in a basic network. After that, a joint representation of these pyramidal features is feed into the loss function for learning discriminative features for vehicle Re-ID. We validated the proposed approach on a commonly used vehicle database i.e., VehicleID. Extensive experimental results show that the proposed method is superior to multiple state-of-the-art vehicle Re-ID methods.
This work was supported in part by the National Natural Science Foundation of China under the grants 61871434, 61602191, and 61802136, in part by the Natural Science Foundation for Outstanding Young Scholars of Fujian Province under the grant 2019J06017, in part by the Natural Science Foundation of Fujian Province under the grant 2017J05103, in part by the Fujian-100 Talented People Program, in part by High-level Talent Innovation Program of Quanzhou City under the grant 2017G027, in part by the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University under the grants ZQN-YX403 and ZQN-PY418, and in part by the High-Level Talent Project Foundation of Huaqiao University under the grants 14BS201, 14BS204 and 16BS108, and in part by the Subsidized Project for Postgraduates Innovative Fund in Scientific Research of Huaqiao University.
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Lin, X., Zeng, H., Hou, J., Zhu, J., Chen, J., Ma, KK. (2020). Vehicle Re-identification Using Joint Pyramid Feature Representation Network. In: Li, B., Zheng, J., Fang, Y., Yang, M., Yan, Z. (eds) IoT as a Service. IoTaaS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-44751-9_44
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