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Pose-driven deep convolutional model for person re-identification
Chi Su1; Jianing Li1; Shiliang Zhang1; Junliang Xing2; Wen Gao1; Qi Tian3
Conference NameIEEE International Conference on Computer Vision
Conference Date2017
Conference PlaceVenice, Italy
AbstractFeature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Extensive experimental analyses and results on three popular datasets demonstrate significant performance improvements of our model over all published state-of-the-art methods.
Indexed ByEI
Document Type会议论文
Affiliation1.School of Electronics Engineering and Computer Science, Peking University, Beijing 100871
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
3.Department of Computer Science, University of Texas at San Antonio, San Antonio
Recommended Citation
GB/T 7714
Chi Su,Jianing Li,Shiliang Zhang,et al. Pose-driven deep convolutional model for person re-identification[C],2017.
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