Bypassing BigBackground: An efficient hybrid background modeling algorithm for embedded video surveillance

As computer vision algorithms move to embedded platforms within distributed smart camera systems, greater attention must be placed on the efficient use of storage and computational resources. Significant savings can be made in background modeling by identifying large areas that are homogenous in color and sparse in activity. This paper presents a pixel-based background model that identifies such areas, called BigBackground, from a single image frame for fast processing and efficient memory usage. We use a small 15 color palette to identify and represent BigBackground colors. Results on a variety of outdoor and standard test sequences show that our algorithm performs in real-time on an embedded processing platform (the eBox-2300) with reliable background/foreground segmentation accuracy.