A Novel Self Organizing Network to Perform Fast Moving Object Extraction from Video Streams

Image segmentation is a critical task in computer vision. In the context of motion detection, a very popular segmentation approach is background subtraction which consists in classifying the pixels as background and foreground. Then, the foreground pixels are grouped together to find objects, this task is known as object extraction. There are several different approaches to object extraction (e.g. connected component labeling, morphological operators, size thresholding and clustering) amongst them, cluster based approaches are, probably, the ones with a stronger theoretical foundation. However, their application to object extraction is difficult because of three problems: a) need to know the number of objects to be detected beforehand, b) high sensibility to initialization due to a trend to get stuck in local minima and c) high complexity which makes difficult their application in real-time. This paper proposes an algorithm which aims to combine the strong theoretical foundations of clustering with the speed of other approaches. This is possible due to the introduction of a novel self organizing network (SON) which has a robust initialization schema and is able to find the number of clusters in the image. The algorithm has a time complexity of order NM where N is the number of foreground pixels in the image and M is the number of nodes in the SON