Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939015 | Pattern Recognition | 2018 | 18 Pages |
Abstract
This paper proposes a novel descriptor called Maximal Granularity Structure Descriptor (MGSD) for feature representation and an effective metric learning method called Generalized Multi-view Discriminant Analysis based on representation consistency (GMDA-RC) for person re-identification (Re-ID). The proposed descriptor of MGSD captures rich local structural information from overlapping macro-pixels in an image, analyzes the horizontal occurrence of multi-granularity and maximizes the occurrence to extract a robust representation for viewpoint changes. As a result, the proposed descriptor of MGSD can obtain rich person appearance whilst being robust against different condition changes. Besides, considering multi-view information, we present a new GMDA-RC for different views, inspired by the observation that different views share similar data structures. The proposed metric learning method of GMDA-RC seeks multiple discriminant common spaces for multiple views by jointly learning multiple view-specific linear transforms. Finally, we evaluate the proposed method of (MGSD+GMDA-RC) on three publicly available person Re-ID datasets: VIPeR, CUHK-01 and Wide Area Re-ID dataset (WARD). For the VIPeR and CUHK-01, the experimental results show that our method significantly outperforms the state-of-the-art methods, achieving the rank-1 matching rates of 67.09%, 70.61%, and the improvements of 17.41%, 5.34%, respectively. For the WARD, we consider different pairwise camera views (camera 1-2, camera 1-3, camera 2-3) and our method can achieve the rank-1 matching rates of 64.33%, 59.42%, 70.32%, increasing of 5.68%, 11.04%, 9.06% compared with the state-of-the-art methods, respectively.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Zhao Cairong, Wang Xuekuan, Miao Duoqian, Wang Hanli, Zheng Weishi, Xu Yong, Zhang David,