Improved Gaussian mixtures for robust object detection by adaptive multi-background generation

Mahfuzul Haque, Manzur Murshed, Manoranjan Paul

Research output: Book chapter/Published conference paperConference paperpeer-review

61 Citations (Scopus)


Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. Recently the convergence speed of this approach is improved and a relatively robust statistical framework is proposed by Lee (PAMI, 2005). However, object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and inability to handle intrinsic background motion. This paper proposes an effective technique to eliminate these drawbacks by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more believe-to-be backgrounds. Experimental results on two benchmark datasets confirm that the object quality of the proposed technique is superior to that of Leepsilas technique at any model learning rate.
Original languageEnglish
Title of host publicationInternational Conference on Pattern Recognition
Place of PublicationUSA
Number of pages4
ISBN (Electronic)9781424421749
Publication statusPublished - 2008
EventICPR 2008: 19th International Conference - Florida, USA, New Zealand
Duration: 08 Dec 200811 Dec 2008


ConferenceICPR 2008: 19th International Conference
Country/TerritoryNew Zealand


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