Edge noise removal in multimodal background modeling techniques
Jee Whan Choi, Senyo Apewokin, B. E. Valentine, D. Scott Wills, L. M. Wills · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Traditional video scene analysis depends on accurate background modeling techniques to segment objects of interest. Multimodal background models such as Mixture of Gaussian (MOG) and Multimodal Mean (MM) are capable of handling dynamic scene elements and incorporating new objects into the background. Due to the adaptive nature of these techniques, new pixels have to be observed consistently over time before they can be incorporated into the background. However, pixels in the boundary between two colors tend to fluctuate more, creating false positive pixels that result in less accurate foreground segmentation. To correct this, a simple and computationally efficient edge detection based algorithm is proposed. On average, approximately 70 percent of these false positives can be eliminated with little computational overhead.