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

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