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Author:

Li, Youjiao (Li, Youjiao.) | Zhuo, Li (Zhuo, Li.) | Hu, Xiaochen (Hu, Xiaochen.) | Zhang, Jing (Zhang, Jing.) (Scholars:张菁)

Indexed by:

CPCI-S

Abstract:

Person re-identification is one of the hot topics in computer vision. How to design a robust feature representation to identify pedestrians is a key problem for person re-identification. In this paper, a feature representation based on Multi-Statistics Cascade on Pyramid (MSCP) is proposed for person re-identification. The MSCP feature is composed of deep PCA network feature and hand-crafted features of Local Maximal Occurrence (LOMO) feature and color correlogram. MSCP can characterize the pedestrian images precisely from both global and local views. The Cross-view Quadratic Discriminant Analysis (XQDA) is employed to learn the distance metric of MSCP features. And then a novel re-identification method based on MSCP and XQDA is achieved. Experimental results on VIPeR Dataset demonstrate that our proposed method can achieve superior identification performance compared with six state-of-art methods.

Keyword:

deep feature hand-crafted feature Multi-Statistics Cascade on Pyramid person re-identification

Author Community:

  • [ 1 ] [Li, Youjiao]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 2 ] [Zhuo, Li]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 3 ] [Hu, Xiaochen]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 4 ] [Zhang, Jing]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 5 ] [Li, Youjiao]Shandong Univ Technol, Dept Comp Sci & Technol, Zibo, Peoples R China
  • [ 6 ] [Zhuo, Li]Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing, Peoples R China

Reprint Author's Address:

  • [Li, Youjiao]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China;;[Li, Youjiao]Shandong Univ Technol, Dept Comp Sci & Technol, Zibo, Peoples R China

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Source :

PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATICS AND COMPUTING (PIC), VOL 1

ISSN: 2474-0209

Year: 2016

Page: 224-227

Language: English

Cited Count:

WoS CC Cited Count: 5

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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