Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0610001(2023)
Person Re-Identification Based on Pose Estimation with Feature Similarity
This paper proposes a part aligned multiscale fusion network, based on a human pose estimation algorithm and similarity matrix to address the misalignment problem of local features caused by complex person re-identification scenes and difficulty in extracting invariant person features in cluttered backgrounds. The proposed network introduces pose-estimation algorithms to construct aligned local features and integrates low-level local features and high-level global features through a multibranch structure. In addition, the feature similarity matrix divides the global features into the similarity-guided background and foreground branches and uses the regional-level triplet loss to extract person features robust to complex backgrounds. Extensive experiments are conducted for four datasets (Market-1501, DukeMTMC-ReID, CUHK03, and MSMT17). The proposed method achieves state-of-the-art performance. In particular, it improves the accuracy of first hit accuracy by 1.4 percentage points and mean average precision by 3.4 percentage points on the most challenging MSMT17 dataset.
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Rui Li, Min Jiang. Person Re-Identification Based on Pose Estimation with Feature Similarity[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0610001
Category: Image Processing
Received: Nov. 3, 2021
Accepted: Jan. 11, 2022
Published Online: Mar. 7, 2023
The Author Email: Jiang Min (minjiang@jiangnan.edu.cn)