Xu Boqiang, He Lingxiao, Liang Jian, Sun Zhenan
IEEE Trans Image Process. 2022;31:4651-4662. doi: 10.1109/TIP.2022.3186759. Epub 2022 Jul 12.
One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e. , the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this paper, we propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its k -nearest neighbors in the gallery to recover the complete features. Extensive experiments across different person Re-ID datasets, including occluded, partial and holistic datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly outperforms state-of-the-art results by at least 6.2% Rank- 1 accuracy and 7.2% mAP scores on the challenging Occluded-Duke dataset.
挑战行人重识别（Re-ID）的一个主要问题是在捕获的行人身上普遍存在遮挡情况。遮挡行人的Re-ID问题存在两个主要挑战，即特征匹配过程中噪声的干扰以及遮挡带来的行人信息丢失。在本文中，我们提出了一种名为特征恢复Transformer（FRT）的新方法，以同时解决这两个挑战，该方法主要由可见性图匹配和特征恢复Transformer组成。为了减少特征匹配过程中噪声的干扰，我们主要关注在两幅图像中都出现的可见区域，并开发了一种可见性图来计算相似度。针对第二个挑战，基于所开发的图相似度，对于每个查询图像，我们提出了一种恢复Transformer，它利用图库中其k近邻的特征集来恢复完整特征。在不同的行人Re-ID数据集上进行的广泛实验，包括遮挡、部分和整体数据集，证明了FRT的有效性。具体而言，在具有挑战性的Occluded-Duke数据集上，FRT的Rank-1准确率至少比当前最优结果高出6.2%，mAP分数高出7.2%。
