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作者:

Youjiao, Li (Youjiao, Li.) | Li, Zhuo (Li, Zhuo.) | Jiafeng, Li (Jiafeng, Li.) | Jing, Zhang (Jing, Zhang.) (学者:张菁)

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摘要:

A two-level hierarchical scheme for video-based person re-identification (re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically, given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network (CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover, local features of Local maximal occurrence (LOMO) of the walking cycle are extracted to represent the pedestrian' s appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and iLIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose, lighting, and camera viewpoint.

关键词:

Video&#8208 identification based person re&#8208 Convolutional neural network Walking cycle extraction Key frame detection

作者机构:

  • [ 1 ] [Youjiao, Li]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Zhuo]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 3 ] [Jiafeng, Li]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 4 ] [Jing, Zhang]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 5 ] [Youjiao, Li]Beijing Univ Technol, Coll Microelect, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Li, Zhuo]Beijing Univ Technol, Coll Microelect, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 7 ] [Jiafeng, Li]Beijing Univ Technol, Coll Microelect, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 8 ] [Jing, Zhang]Beijing Univ Technol, Coll Microelect, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 9 ] [Youjiao, Li]Shandong Univ Technol, Coll Comp Sci & Technol, Zibo 255000, Peoples R China

通讯作者信息:

  • [Li, Zhuo]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China;;[Li, Zhuo]Beijing Univ Technol, Coll Microelect, Fac Informat Technol, Beijing 100124, Peoples R China

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来源 :

CHINESE JOURNAL OF ELECTRONICS

ISSN: 1022-4653

年份: 2021

期: 2

卷: 30

页码: 289-295

1 . 2 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:87

JCR分区:4

被引次数:

WoS核心集被引频次: 3

SCOPUS被引频次: 3

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

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