  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.
