Occlusion activity detection algorithm using Kalman filter for detecting occluded multiple objects

Heungkyu Lee, Hanseok Ko

Research output: Contribution to journalConference articlepeer-review


This paper proposes the detection method of occluded moving objects using occlusion activity detection algorithm. When multiple objects are occluded between them, a simultaneous feature based tracking of multiple objects using tracking filters fails. To estimate feature vectors such as location, color, velocity, and acceleration of a target are critical factors that affect the tracking performance and reliability. To resolve this problem, the occlusion activity detection algorithm is addressed. Occlusion activity detection method provides the occlusion status of next state using the Kalman prediction equation. By using this predicted information, the occlusion status is verified once again in its current state. If the occlusion status is enabled, an object association technique using a partial probability model is applied. For an experimental evaluation, the image sequences for a scenario in which three rectangles are moving within the image frames are made and evaluated. Finally, the proposed algorithms are applied to real image sequences. Experimental results in a natural environment demonstrate the usefulness of the proposed method.

Original languageEnglish
Pages (from-to)139-146
Number of pages8
JournalLecture Notes in Computer Science
Issue numberI
Publication statusPublished - 2005
Event5th International Conference on Computational Science - ICCS 2005 - Atlanta, GA, United States
Duration: 2005 May 222005 May 25

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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