An Efficient Hybrid Approach for Detecting and Tracking Moving Objects for Video Surveillance - Shanmugappriya, S.; Mahesh, K. (IJRDO -Journal of Computer Science Engineering, 2017)
In visual surveillance applications, detection and identification of an object in video frames stream is to be considered as critical task. In recent years, researchers designed many algorithms named Background Subtraction, Optical Flow and Temporal Differencing, etc., to detecting the real time moving object. Among these, background subtraction algorithm used widely because it has frame difference, approximate median, Gaussian mixture. The aim of the proposed work is to detect real time moving objects could be done efficacy with the help of mixture of Gaussian algorithm which requires less in memory and produce good in result. Here, unwanted noise can be removed by using wiener filter, select YCbCr color space for detecting the foreground object which is fit for light / indoor shadow. Further, test has been made on various video clip and from the experimental results, complexity is easily reduce as well as foreground objects are detected without noise by using Gaussian mixture model.