@inproceedings{78bd0642f3474a3396a7a2ed9cba9208,
title = "A Local Rotation Transformation Model for Vehicle Re-Identification",
abstract = "The vehicle re-identification (V-ReID) task is critical in urban surveillance and can be used for a variety of purposes. We propose a novel augmentation method to improve the V-ReID performance. Our deep learning framework mainly consists of a local rotation transformation and a target selection module. In particular, we begin by using a random selection method to locate a local region of interest in an image sample. Then, a parameter generator network is in charge of generating parameters for further image rotation transformation. Finally, a target selection module is used to retrieve the augmented image sample and update the parameter generator network. Our method is effective on VeRi-776 and VehicleID datasets, it shows that we achieve considerable competitive results with the current state-of-the-art.",
keywords = "Local region, Local rotation transformation, Parameter generator network, Target selection, Vehicle re-identification",
author = "Yanbing Chen and Wei Ke and Hao Sheng and Zhang Xiong",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022 ; Conference date: 24-11-2022 Through 26-11-2022",
year = "2022",
doi = "10.1007/978-3-031-19208-1\_7",
language = "英语",
isbn = "9783031192074",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "76--87",
editor = "Lei Wang and Michael Segal and Jenhui Chen and Tie Qiu",
booktitle = "Wireless Algorithms, Systems, and Applications - 17th International Conference, WASA 2022, Proceedings",
address = "德国",
}