GM-PHD-Based multi-target visual tracking using entropy distribution and game theory

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

73 Scopus Citations
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Author(s)

  • Xiaolong Zhou
  • Youfu Li
  • Bingwei He
  • Tianxiang Bai

Detail(s)

Original languageEnglish
Article number6678810
Pages (from-to)1064-1076
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume10
Issue number2
Online published5 Dec 2013
Publication statusPublished - May 2014

Abstract

Tracking multiple moving targets in a video is a challenge because of several factors, including noisy video data, varying number of targets, and mutual occlusion problems. The Gaussian mixture probability hypothesis density (GM-PHD) filter, which aims to recursively propagate the intensity associated with the multi-target posterior density, can overcome the difficulty caused by the data association. This paper develops a multi-target visual tracking system that combines the GM-PHD filter with object detection. First, a new birth intensity estimation algorithm based on entropy distribution and coverage rate is proposed to automatically and accurately track the newborn targets in a noisy video. Then, a robust game-theoretical mutual occlusion handling algorithm with an improved spatial color appearance model is proposed to effectively track the targets in mutual occlusion. The spatial color appearance model is improved by incorporating interferences of other targets within the occlusion region. Finally, the experiments conducted on publicly available videos demonstrate the good performance of the proposed visual tracking system. © 2012 IEEE.

Research Area(s)

  • Birth intensity estimation, Gaussian mixture probability hypothesis density (GM-PHD) filter, multi-target visual tracking (MTVT), Mutual occlusion handling

Citation Format(s)