Abstract

Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.

Original languageEnglish
Title of host publication2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467366649
DOIs
Publication statusPublished - 21 Mar 2016
Event2nd International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Bandung, Bali, Indonesia
Duration: 16 Nov 201519 Nov 2015

Publication series

Name2015 International Conference on Information Technology Systems and Innovation, ICITSI 2015 - Proceedings

Conference

Conference2nd International Conference on Information Technology Systems and Innovation, ICITSI 2015
Country/TerritoryIndonesia
CityBandung, Bali
Period16/11/1519/11/15

Keywords

  • Corrected Background Weighted Histogram
  • Mean Shift
  • Normalized Cross Correlation
  • Scaling
  • Visual Object tracking

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