This paper discusses the problem of segmenting foreground objects with apertures or discontinuities under camouflage effect and the optical physics model is introduced into foreground detection. A moving foreground objects extraction method based on color invariants is proposed in which color invariants are used as descriptors to model the background and do the foreground segmentation. It makes full use of the color spectral information and spatial configuration. Experimental results demonstrate that the proposed method performs well in various situations of color similarity and meets the demand of real-time performance.
Robust detection of moving objects in image sequences is an essential part of many vision applications. However, it is not easily achievable with a moving camera since the camera and moving objects motions are mixed together. In this paper we propose a method to detect moving objects under a moving camera. The camera ego-motion is compensated by the corresponding feature sets. The difference image between two consecutive images that ego-motion is compensated is transformed into a binary image using k-means algorithm. According to the clustering results, the region of interest where moving objects are likely to exist is searched by the projection approach. Then local threshold and contour filling methods are applied to detect the accurate moving objects. Experimental results on real image sequences demonstrate that our method can get intact moving objects in the case of a moving camera efficiently.