Joint uneven channel information network with blend metric loss for person re-identification

Abstract

Person re-identification, one of the most challenging tasks in the field of computer vision, aims to recognize the same person cross different cameras. The local feature information has been proved that can improve performance efficiently. Image horizontal even division and pose estimation are two popular methods to extract the local feature. However, the former may cause misalignment, the latter needs much calculation. To fill this gap and improve performance, an efficient strategy is proposed in this work. First, a joint uneven channel information network consisting of an uneven channel information extraction network and a channel information fusion network is designed. Different from the traditional image division, the former can divide images horizontally and unevenly with strong alignment based on weak pose estimation, and extract multiple channel information. The latter can joint channel information based on channel validity and generate an efficient similarity descriptor. To optimize the joint uneven channel information network efficiently, this work proposes a blend metric loss. The extra image information is utilized to dynamically adjust the penalty for the sample distance and the distance margin based on the outlier of the hardest sample to construct i-TriHard loss. Besides, softmax loss and center loss are embedded in the blend metric loss, which can guide the network to learn more discriminative features. Our method achieves 89.6% mAP and 95.9% Rank-1 on Market-1501, 79.9% mAP and 89.4% Rank-1 on DukeMTMC. The proposed method also performs excellently on occluded datasets.