AN IMPROVED BACKGROUND SUBTRACTION ALGORITHM FOR INTELLIGENT VIDEO SURVEILLANCE

This thesis briefly introduces background subtraction which is one of the current advanced techniques for intelligent video surveillance. The paper also presents an improved background subtraction technique for motion detection in the area of intelligent video surveillance. The proposed technique is difference from the state-of-the-art background subtraction algorithms such as Gaussian Mixture Models or Behavior Subtraction, etc. The idea of this enhanced background subtraction is inspired by the ViBe algorithm. In the first step, which is the initialization of background subtraction, it takes samples only in its current location and 3x3 neighborhoods for each pixel. Then compare each pixel with the sampling set to determine whether this pixel belongs to background models or foreground objects. The next step is to update the sampling sets so that the algorithm can adapt to different scenarios. The final procedure is the post-processing method, which has better performance to overcome difficulties. This approach differs from those based upon the classical belief that the samples set of current pixels should be collected according to certain mathematic models. Experimental results of the proposed algorithm are better than earlier techniques that use the ViBe algorithm. Keywords: Background subtraction, computer vision, motion detection, real-time systems, intelligent video surveillance.M.S. in Electrical Engineering, July 201