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arXiv 2004.091642020-05-15cs.CV

VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera

Ruiyan Technology（睿眼科技）

Xiangyu Zhu, Zhenbo Luo, Pei Fu, Xiang Ji

备注：: AICity2020 Challenge, CVPR 2020 workshop, code avaible at github(link in abstract)

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Vehicle re-identification is a challenging task due to high intra-class variances and small inter-class variances. In this work, we focus on the failure cases caused by similar background and shape. They pose serve bias on similarity, making it easier to neglect fine-grained information. To reduce the bias, we propose an approach named VOC-ReID, taking the triplet vehicle-orientation-camera as a whole and reforming background/shape similarity as camera/orientation re-identification. At first, we train models for vehicle, orientation and camera re-identification respectively. Then we use orientation and camera similarity as penalty to get final similarity. Besides, we propose a high performance baseline boosted by bag of tricks and weakly supervised data augmentation. Our algorithm achieves the second place in vehicle re-identification at the NVIDIA AI City Challenge 2020.

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