Graph Convolutional Network with Triplet Attention learning for Person Re-Identification.
| Author | Saber S.; Amin K.; Plawiak P.; Tadeusiewicz R.; Hammad M. |
|---|---|
| Title | Graph Convolutional Network with Triplet Attention learning for Person Re-Identification. |
| Journal | Information Sciences |
| Year | 2022 |
| Status | Published |
| DOI | https://doi.org/10.1016/j.ins.2022.10.105 |
| URL | https://www.sciencedirect.com/science/article/pii/S0020025522012257 |
| Abstract | <p>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.</p> |
| ISSN | 0020-0255 |