Abstract: Background subtraction of video image is critical for detecting and tracking moving objects. Because of variety of video image and complicated background, it is difficult for a method to detect moving objects for all video images. This paper proposes the 1-D maximum entropy of image to accomplish the object detection of video images with dynamical and complicated background. The algorithm obtains the gray value which makes the entropy of object and background maximum, retains most information of moving object. Compared with frame difference method and Gaussian mixture background modeling method, 1-D maximum entropy method obtains the accurate object with shorter computational time, and much information of object. It is suitable for detecting objects of the videos with dynamical and variable background in real time.

Key words: one-dimensional maximum entropy, background subtraction, moving object, dynamical background, real-time detection