SFMNet: Self-guided Feature Mining Network for Vehicle Re-identification

Abstract

Vehicle Re-identification (Re-ID) is very important in intelligent transportation and video surveillance. Most previous works mainly extract discriminative features from the visual appearance of vehicles. However, the irrelevant background in the image will affect the extracted features, and it is also important to extract fine-grained features because the vehicles have the same style. Previous works extracted robust features by manual annotation, which was inefficient. To solve this problem, this paper proposes a self-guided feature mining network (SFMNet) that can eliminate background intervention without annotation, while mining fine-grained feature information of vehicles. In order to eliminate background intervention and mine fine-grained features without resorting to annotations, we have carefully designed two novel modules. (i) The noise patch filter (NPF) module can identify the image background without annotation, and filter out the background to eliminate the intervention of the background on the image features. (ii) The salient feature extraction (SFE) module uses self-attention as a guide without vehicle component annotation to mine fine-grained features and enhance discriminative visual cue features. Extensive experiments demonstrate the effectiveness of our method, and we achieve state-of-the-art results on three publicly available datasets, including Veri-776, VehicleID, and VERI-WILD.