Decision fusion of shape and motion information based on bayesian framework for moving object classification in image sequences

  • Authors:

  • Heungkyu Lee;JungHo Kim;June Kim

  • Affiliations:

  • Dept. of Computer Science, Seokyeong University, Seoul, Korea;Republic of Korea Navy 1st Fleet, Korea;Dept. of Information and Communication Engineering, Seokyeong University, Seoul, Korea

  • Venue:
  • ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
  • Year:
  • 2006

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Abstract

This paper proposes decision fusion method of shape and motion information based on Bayesian framework for object classification in image sequences. This method is designed for intelligent information and surveillance guard robots to detect and track a suspicious person and vehicle within a security region. For reliable and stable classification of targets, multiple invariant feature vectors to more certainly discriminate between targets are required. To do this, shape and motion information are extracted using Fourier descriptor, gradients, and motion feature variation on spatial and temporal images, and then local decisions are performed respectively. Finally, global decision is done using decision fusion method based on Bayesian framework. The experimental results on the different test sequences showed that the proposed method obtained good classification result than any other ones using neural net and other fusion methods.