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.
BT - Information Sciences DO - https://doi.org/10.1016/j.ins.2022.10.105 LA - eng N2 -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.
PY - 2022 T2 - Information Sciences TI - Graph Convolutional Network with Triplet Attention learning for Person Re-Identification. UR - https://www.sciencedirect.com/science/article/pii/S0020025522012257 SN - 0020-0255 ER -