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

Abstract

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 illumination, pose changes among camera views, cluttered backgrounds, and inaccurate detections. 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 accuracy of 92.98% for rank-1. According to the results, our method ameliorates quantitative and qualitative person re-ID methods.