Segmentation and Denoising of Surveillance Video Using Adaptive Gmm Towards Effective Video Retrieval
In modern times, the advanced technologies in camera have made the video acquisition more feasible. Data sets collected by image sensors are generally contaminated by noise. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest. Surveillance sequences do not only contain background, but also motional foreground. Variety of algorithms has been developed to reduce video noise. Simply inter-frame averaging will lead to motion blurring. Such result will be avoided if the authors can separate foreground and background, and make background to be averaged only. Recently, a number of video object segmentation algorithms have been discussed and unfortunately most existing segmentation algorithms are not adequate and robust enough to process noisy video sequences.