A Novel Background Modeling Based on Keyframe and Particle Shape Property for Surveillance Video

With the development of industrial informatization, video processing technology is receiving more and more attention. Extracting background is a prerequisite for many video processing techniques, so video background modeling technology is becoming highly sought-after. Currently, there are a variety of approaches to estimating background; however, many of these methods have the fault of not being able to accurately distinguish between foreground and background, especially when objects move slowly or remain still for a period of time. In this paper, a novel background modeling scheme is proposed for surveillance video, based on keyframe and particle shape properties. The model consists of three parts: the first part is to reduce running time and eliminate the ghost phenomenon caused by adjacent redundant frames by extracting keyframe and dividing the extracted frames into several groups; the second part includes three steps, computing binarized difference, characterizing the binarized difference and screening the difference where a quadruple, composed of particle shape properties, is designed to quantitatively describe binarized differences; the third part involves generating the temporary background and updating the temporary background according to the similarity of data obtained at the newly proposed locations. Experiment results on SBMnet and SBI datasets and comparisons with some emerging algorithms show that the performance of the proposed model is superior or comparable to the other state-of-the-art methods particularly when dealing with stationary objects. Furthermore, the proposed method ranks as the second for intermittent category video, compared to the other 31 state-of-art methods. Moreover, the speed of the proposed method, 5.38 Frames Per Second (FPS) for SBMnet dataset and 10.94 FPS for SBI dataset, is faster than most public methods.