Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes
Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

A dynamic learning rate Gaussian mixture model (GMM) algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance, especially in the presence of sudden illumination changes. The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems. To solve this problem, a mixture Gaussian model has been built for each pixel in the video frame, and according to the scene change from the frame difference, the learning rate of GMM can be dynamically adjusted. The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate. The method was tested on a certain dataset, and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.
A dynamic learning rate Gaussian mixture model (GMM) algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance, especially in the presence of sudden illumination changes. The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems. To solve this problem, a mixture Gaussian model has been built for each pixel in the video frame, and according to the scene change from the frame difference, the learning rate of GMM can be dynamically adjusted. The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate. The method was tested on a certain dataset, and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.