Skip to main content

Partial Least Squares-Based Incremental PCA for Robust Human Detection and Tracking

Buy Article:

$107.14 + tax (Refund Policy)

Human tracking is a major issue in computer vision where the challenging part is to track a subject under an uncontrolled environment. Most of the existing algorithms are not able to perform well in such condition due to variation in the human appearance caused by covariates like clothing and the illumination changes. One reason these algorithms fail to perform well is because of the use of a fixed appearance model for the human object. The fixed model is limited and insufficient to cope with the constant appearance change in the image stream. Overtime, the tracking result will be drifted away from the trajectory and lost track of the actual target. In this paper, a method coined as Partial Least Squares-based Incremental PCA (PI-PCA) is proposed to address the human detection and tracking problem. A human detection method based on partial least squares (PLS) regression is used to locate the occurrence of a human subject in the video frame. Once a human object is detected, incremental PCA will be used to track this subject over the video stream. A forgetting factor is used to follow the tracking history. Once the sign of a drift is detected, PLS will be called to correct and re-lock the actual position of the target object. The proposed method is an improvement over the existing incremental learning algorithms as it introduces a corrective mechanism in the tracking process. Empirical tests demonstrate that the proposed PI-PCA method adapts well to appearance change of the human object over a long video stream with substantial motion switch.

Keywords: Human Detection; Human Tracking; Incremental PCA; Partial Least Squares Regression

Document Type: Research Article

Affiliations: Faculty of Information and Science Technology, Multimedia University, Melaka 75450, Malaysia

Publication date: February 1, 2018

More about this publication?
  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content