to segment moving foreground from background , where the moving foreground is of most interest to the viewer , this method uses three detection algorithms as the input to a neural network . the multiple cues used are focus , intensity , and motion . the neural network consists of a two - layered neural network . focus and motion measurements are taken from high frequency data , edges ; whereas , intensity measurements are taken from low frequency data , object interiors . combined , these measurements are used to segment a complete object . results indicate that moving foreground can be segmented from stationary foreground and moving or stationary background . the neural network segments the entire object , both interior and exterior , in this integrated approach . results also demonstrate that combining cues allows flexibility in both type and complexity of scenes . integration of cues improves accuracy in segmenting complex scenes containing both moving foreground and background . good segmentation yields bit rate savings when coding the object of interest , also called the video object in mpeg4 . this method combines simple measurements to increase segmentation robustness .