TY - GEN
T1 - A Local Rotation Transformation Model for Vehicle Re-Identification
AU - Chen, Yanbing
AU - Ke, Wei
AU - Sheng, Hao
AU - Xiong, Zhang
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Local region
KW - Local rotation transformation
KW - Parameter generator network
KW - Target selection
KW - Vehicle re-identification
UR - http://www.scopus.com/inward/record.url?scp=85142714695&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-19208-1_7
DO - 10.1007/978-3-031-19208-1_7
M3 - Conference contribution
AN - SCOPUS:85142714695
SN - 9783031192074
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 76
EP - 87
BT - Wireless Algorithms, Systems, and Applications - 17th International Conference, WASA 2022, Proceedings
A2 - Wang, Lei
A2 - Segal, Michael
A2 - Chen, Jenhui
A2 - Qiu, Tie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022
Y2 - 24 November 2022 through 26 November 2022
ER -