Efficient background subtraction method based on fast independent component analysis in video-surveillance

Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the demixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the demixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and postprocessing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change detection datasets CDnet 2012 and CDnet 2014 using performance parameters such as recall, precision and 𝐹𝑚𝑒𝑎𝑠𝑢𝑟𝑒. Experimental results show that our algorithm can detect effectively and accurately the moving objects in several background and foreground conditions compared to other methods in literature with real-time frame rate.