Automatic moving object segmentation based on spatial and temporal information

In this paper, a fast and robust automatic moving object segmentation system is introduced. The system tries to integrate the spatial and temporal segmentation results to separate moving objects from still background in video frames. For the spatial segmentation, the intensity histogram of each frame is smoothed using numerical diffusion algorithm, producing a multimodal probability density function consists of Gaussian kernels. Then, Laplacian operator is applied to the smoothed histogram in order to identify the dominant intensity, and the frame is segmented into separate regions with the dominant intensity. This new method can produce stable spatial region segmentation results with relatively cheap computational cost. According to the segmented regions, moving objects can be detected by a statistic scene change detection algorithm. Experimental results show that the moving object segmentation system can segment the object boundaries accurately throughout the entire video sequence.