VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
This is an incremental improvement for vehicle re-identification in surveillance and security applications.
The paper tackled vehicle re-identification by addressing biases from similar backgrounds and shapes, proposing VOC-ReID to use vehicle-orientation-camera triplets and penalize orientation and camera similarities, achieving second place in the NVIDIA AI City Challenge 2020.
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.