Proc. SPIE. 6813, Image Processing: Machine Vision Applications
KEYWORDS: Edge detection, Detection and tracking algorithms, Image segmentation, Video, Digital filtering, Linear filtering, Video surveillance, Light sources and illumination, Motion models, Positron emission tomography
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.