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Projects / Person Re-Identification

Person re-identification handles pedestrian matching and ranking across non-overlapping camera views. It has many important applications in video surveillance by saving a lot of human efforts on exhaustively searching for a person from large amounts of video sequences.

We focus on feature design, attribute and salience learning, metric learning, and manifold modeling.

Person Re-Identification by Manifold Ranking
C. C. Loy, C. Liu, and S. Gong. In Proceedings of IEEE International Conference on Image Processing (Poster, ICIP 2013)
PDF Dataset

Locally Aligned Feature Transforms across Views
W. Li and X. Wang. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Poster, CVPR 2013)
PDF

Unsupervised Salience Learning for Person Re-identification
R. Zhao, W. Ouyang and X. Wang. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Poster, CVPR 2013)
PDF Project Page

Person Re-Identification: What Features are Important?
C. Liu, S. Gong, C. C. Loy, and X. Lin. In Proceedings of European Conference on Computer Vision, International Workshop on Re-Identification (Poster, ECCV 2013)
PDF Data partition and CMC curves

Human Reidentification with Transferred Metric Learning
W. Li, R. Zhao and X. Wang. In Proceedings of Asian Conference on Computer Vision (Poster, ACCV 2012)
PDF

Shape and Appearance Context Modeling
X. Wang, G. Doretto, T. Sebastian, J. Rittscher, and P. Tu. in Proceedings of IEEE International Conference on Computer Vision (ICCV) 2007.
PDF


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