Image background reconstruction by Gaussian mixture based model reinforced with temporal-spatial confidence

Background reconstruction from an image sequence is an important topic in image processing. However, most existing background reconstruction algorithms do not produce results as good as expected when applied to complex images. The Gaussian mixture model is frequently utilized to represent image features and used to reconstruct background for complex image. A Gaussian mixture-based model for background restoration algorithm is proposed, which evaluates the temporal confidence as well as spatial confidence value to get multiple most reliable models to assess whether a pixel of the image of a background one or a foreground one. During the process, a Sarsa(λ) is utilized to achieve automatic adaption by interaction with the image during the processing to get maximal-reinforced temporal–spatial confidence. To obtain better reconstruction results, a series of pre-processing methods, such as shadow detection and removing, sunshine change relieving and sudden noise detection and removing, are also used before background reconstructing to wipe off negative interface suffered by noises, e.g. shadow, daylight change, and sudden noise. The testing results show that our proposed algorithms work well in background reconstruction.