An improved Gaussian mixture modeling algorithm combining foreground matching and short-term stability measure for motion detection

Abstract

Although deep learning has been successfully applied in image analysis, the conventional Gaussian Mixture Modeling (GMM) method still has great potential for multi-mode motion detection because it does not require the support of specialized hardware such as GPUs and massive training data. Under the framework of GMM, this paper combines foreground matching and short-term stability measure to detect slow-moving objects. Foreground models built and updated using the detected foreground pixels have the priority to match potential foreground in the incoming pixels. Meanwhile, the pixel-level stability is measured to make sure that an integrated foreground is detected when a dynamic foreground process is followed. The combination of foreground matching and short-term stability measure greatly improves GMM’s tolerance to slow-moving objects. The quantitative evaluation demonstrates the effectiveness of the proposed algorithm to robustly detect slow-moving objects under a variety of real environments with distracting motions such as illumination changes.