VISUAL TRACKING with AUTOMATIC CONFIDENT REGION EXTRACTION

T. A.O. Yang, Jing Li, Quan Pan, Yong Mei Cheng

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this work, a novel efficient algorithm for visual object tracking in complex conditions is proposed. The main component of this work includes two parts: Bayesian decision based confident region extraction, and mean shift iteration based tracking. A unique characteristic of the proposed algorithm is that instead of tracking the entire object, the method automatically extracts the confident region of the object through fusing multiple cues in the Bayesian framework. Those cues contain object's color feature, motion character, and dynamic surrounding color information. We tested the performance of the algorithm with video sequences under difficult conditions (complex and dynamic background, fast camera motion, object maneuvering, rotations and partial occlusion) and achieved satisfied results.

Original languageEnglish
Pages (from-to)369-381
Number of pages13
JournalInternational Journal of Image and Graphics
Volume8
Issue number3
DOIs
StatePublished - 1 Jul 2008

Keywords

  • Confident region selection
  • intelligent video surveillance
  • mean shift
  • object tracking

Fingerprint

Dive into the research topics of 'VISUAL TRACKING with AUTOMATIC CONFIDENT REGION EXTRACTION'. Together they form a unique fingerprint.

Cite this