Graph convolutional network with triplet attention learning for person re-identification

Person re-identification (re-ID) is a method that uses several non-overlapping cameras to identify the same individual. Person re-ID has been employed successfully in a diversity of computer vision applications. This task is made more difficult by occlusions, abrupt illumi-nation, pose changes among camera views, cluttered backgrounds, and inaccurate detec-tions. Therefore, we propose a new graph convolutional network with attention modules. This research reveals a new attention network that encompasses the encoder -decoder and the triplet attention module. The proposed attention module employs the self-attention process to achieve potent and discriminatory features by utilizing temporal, spatial, and channel context information. The triplet attention module is utilized to capture cross-dimension dependencies and pedestrian features, and also reduces the impact of the imperfect pedestrian image to remedy the occlusion issue. The encoder-decoder is used to observe the whole-body shape. Experiments on several publicly available datasets reveal that our method has a high degree of generalization and outperforms existing methods. On Market1501, the proposed method outperformed the recent approaches with an accu-racy of 92.98% for rank-1. According to the results, our method ameliorates quantitative and qualitative person re-ID methods.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Person re-identification is a method that uses multiple non-overlapping cameras for identification, and it has been successfully applied in computer vision applications. To address issues such as occlusion, illumination changes, and pose changes, a new graph convolutional network with attention modules is proposed. Experimental results demonstrate the high generalization ability and superior performance of the proposed method.