An efficient adaptive algorithm for motion detection
Hossein Maghsoumi, Davud Asemani, Hadi Amirpour · 2015
Detecting moving objects in video sequences is a vital task in many computer vision applications. Many different algorithms have been proposed to detect moving objects in successive frames. Gaussian Mixture Model (GMM) is a well-known algorithm that is robust against repetitive motions, illumination changes and long-term scene changes. Adaptive Noise Cancelation (ANC) is another algorithm that has significant robustness against shadow, noise, lighting changes, etc. In this paper, a background is made for each frame by GMM method that is used instead of previous frame in ANC algorithm. This background is much similar to the real background than previous frame is used by ANC. Simulation results show that proposed algorithm detects motions much efficiently than other algorithms.