A Hybrid Object Detection Technique from Dynamic Background Using Gaussian Mixture Models

Mahfuzul Haque, Manzur, Murshed, Manoranjan Paul

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

28 Citations (Scopus)
83 Downloads (Pure)


Adaptive background modelling based object detection techniques are widely used in machine vision applications for handling the challenges of real-world multimodal background. But they are constrained to specific environment due to relying on environment specific parameters, and their performances also fluctuate across different operating speeds. On the other side, basic background subtraction (BBS) is not suitable for real applications due to manual background initialization requirement and its inability to handle repetitive multimodal background. However, it shows better stability across different operating speeds and can better eliminate noise, shadow, and trailing effect than adaptive techniques as no model adaptability or environment related parameters are involved. In this paper, we propose a hybrid object detection technique for incorporating the strengths of both approaches. In our technique, Gaussian mixture models (GMM) is used for maintaining an adaptive background model and both probabilistic and basic subtraction decisions are utilized for calculating inexpensive neighbourhood statistics for guiding the final object detection decision. Experimental results with two benchmark datasets and comparative analysis with recent adaptive object detection technique show the strength of the proposed technique in eliminating noise, shadow, and trailing effect while maintaining better stability across variable operating speeds.
Original languageEnglish
Title of host publicationIEEE Workshop on Multimedia Signal Processing (MMSP)
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781424422944
Publication statusPublished - 2008
EventMMSP 2008: 10th Workshop - Cairns, Qld, Australia
Duration: 08 Oct 200810 Oct 2008


WorkshopMMSP 2008: 10th Workshop


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