Video Summarization based on Motion Detection for Surveillance Systems

In this paper a video summarization method based on motion detection has been proposed. Sensor noise (noise of acquisition and digitization) and the illumination changes in the scene are the most limitations of the background subtraction approaches. In order to handle these problems, this paper present an approach based on the combining of the background subtraction and the Structure-Texture-Noise Decomposition. Firstly, each gray-level image of the sequence will be decomposed on three components, Structure, Texture and Noise. The Structure and Texture components of each image of the sequence are taken to generate the background model. The absolute difference used to subtract the background before compute the binary image of moving objects. We, also, propose a video summarization based on the background subtraction results. The generated background model is used to compute the change during all time of the sequence. The experimental results demonstrate that our approach is effective and accurate for moving objects detection and yields a good summarization of the video sequence.