Image-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking
It addresses vehicle re-identification for surveillance systems, but appears incremental as it builds on existing methods with minor improvements.
The paper tackles vehicle re-identification by proposing a model with adaptive attention modules and metadata re-ranking to reduce annotation needs, achieving a mAP of 37.25% on the CVPR AI City Challenge 2020 dataset.
Vehicle Re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation to extract more features for false positive image exclusion. In this paper, we propose a model powered by adaptive attention modules that requires fewer label annotations but still out-performs the previous models. We also include a re-ranking method that takes account of the importance of metadata feature embeddings in our paper. The proposed method is evaluated on CVPR AI City Challenge 2020 dataset and achieves mAP of 37.25% in Track 2.