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Smart city article details
| Title | Orientation-Aware Vehicle Re-Identification Via Synthesis Data Orientation Regression |
|---|---|
| ID_Doc | 41024 |
| Authors | Wang H.; Sun Z. |
| Year | 2023 |
| Published | Lecture Notes in Electrical Engineering, 917 LNEE |
| DOI | http://dx.doi.org/10.1007/978-981-19-3387-5_154 |
| Abstract | Owing to the need for smart city construction, vehicle re-identification (re-ID) has been widely used in the field of computer vision. Given a probe vehicle image, all of the same vehicles need to be found in the gallery data. However, because of the different camera shooting angles and vehicle driving directions, extreme changes in the viewing angle in vehicle images leads to a dissimilarity in shape, resulting in a difference in vision and having a significant impact on the accuracy. To address this issue, we propose a method for eliminating bias between different viewpoints, specifically, orientation regression training is conducted on a free synthetic dataset through the VehicleX engine, orientation-aware features are extracted using the trained network above, and the similarity calculated by the original re-ID feature and the orientation-aware feature are then fused to obtain the final similarity, which can effectively remove the orientation bias that exists with conventional re-ID features. Extensive experiments on two public datasets confirmed the effectiveness of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. |
| Author Keywords | Deep regression; Synthetic data; Vehicle re-identification |
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