Video-Based Person Re-Identification via Self Paced Weighting

Authors

  • Wenjun Huang Wuhan University
  • Chao Liang Wuhan University
  • Yi Yu National Institute of Informatics
  • Zheng Wang Wuhan University
  • Weijian Ruan Wuhan University
  • Ruimin Hu Wuhan University

Keywords:

Video-based Person Re-identification, Self-Paced Learning

Abstract

Person re-identification (re-id) is a fundamental technique to associate various person images, captured by differentsurveillance cameras, to the same person. Compared to the single image based person re-id methods, video-based personre-id has attracted widespread attentions because extra space-time information and more appearance cues that can beused to greatly improve the matching performance. However, most existing video-based person re-id methods equally treatall video frames, ignoring their quality discrepancy caused by object occlusion and motions, which is a common phenomenonin real surveillance scenario. Based on this finding, we propose a novel video-based person re-id method via self paced weighting (SPW). Firstly, we propose a self paced outlier detection method to evaluate the noise degree of video sub sequences. Thereafter, a weighted multi-pair distance metric learning approach is adopted to measure the distance of two person image sequences. Experimental results on two public datasets demonstrate the superiority of the proposed method over current state-of-the-art work.

Downloads

Published

2018-04-26

How to Cite

Huang, W., Liang, C., Yu, Y., Wang, Z., Ruan, W., & Hu, R. (2018). Video-Based Person Re-Identification via Self Paced Weighting. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11857

Issue

Section

Main Track: Machine Learning Applications