MART: Mask-Aware Reasoning Transformer for Vehicle Re-Identification

Abstract

As a significant topic in Intelligent Transportation Systems (ITS), vehicle Re-Identification (Re-ID) has attracted increasing research attention. However, the variation of shooting scenes and the similar appearance among the vehicles with the same type and color lead to large intra-class variances and small inter-class variances, respectively. To address the problems, we propose a novel Mask-Aware Reasoning Transformer (MART) to extract the background-unrelated global features and perspective-invariant local features. The MART contains three effective modules including Foreground Global Features Extraction (FGFE), Mask-guided Local Features Extraction (MLFE) and Cross-images Local Features Reasoning (CLFR). Firstly, due to the complexity of background information in images, identity representation inevitably contains background elements, which may impact identity matching. To address this issue, we propose the FGFE to extract background-independent global features by introducing the mask semantic information to the inputs of Vision Transformer (ViT). Secondly, to fill the gap that the previous local features extraction methods cannot be directly applied to ViT, the MLFE is presented to extract distinctive local features by recombining token features according to vehicle mask. Thirdly, when local components are invisible in the image due to the occlusion problem, the corresponding local information is absent from the image, leading to unreliable local features. To solve this problem, the CLFR is proposed to reason the occluded local features by exploiting the correlation between cross-image local features. We carry out comprehensive experiments to illustrate the effectiveness of the MART on two challenging datasets.