Tracking multiple moving objects using a level-set method

Chia Jung Chang, Jun-Wei Hsieh*, Yung Sheng Chen, Wen Fong Hu

*Corresponding author for this work

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

This paper presents a novel approach to track multiple moving objects using the level-set method. The proposed method can track different objects no matter if they are rigid, nonrigid, merged, split, with shadows, or without shadows. At the first stage, the paper proposes an edge-based camera compensation technique for dealing with the problem of object tracking when the background is not static. Then, after camera compensation, different moving pixels can be easily extracted through a subtraction technique. Thus, a speed function with three ingredients, i.e. pixel motions, object variances and background variances, can be accordingly defined for guiding the process of object boundary detection. According to the defined speed function, different object boundaries can be efficiently detected and tracked by a curve evolution technique, i.e. the level-set-based method. Once desired objects have been extracted, in order to further understand the video content, this paper takes advantage of a relation table to identify and observe different behaviors of tracked objects. However, the above analysis sometimes fails due to the existence of shadows. To avoid this problem, this paper adopts a technique of Gaussian shadow modeling to remove all unwanted shadows. Experimental results show that the proposed method is much more robust and powerful than other traditional methods.

Original languageEnglish
Pages (from-to)101-125
Number of pages25
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume18
Issue number2
DOIs
StatePublished - 1 Mar 2004

Keywords

  • Background compensation
  • Level-set methods
  • Object-relation table
  • Shadow elimination
  • Speed function
  • Video surveillance

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