PROCESSING VIDEOS USING PARALLEL COMPUTING: A NOVEL APPROACH

In this paper, the proposed framework is presented that supports acquiring high-resolution video’s from the low-resolution. The high-resolution videos could be used in tagging, identifying and tracking people. We concentrate on two aspects. One, data simplification method as the algorithm required for conversion large amount of data processing which is run in parallel. Second, is building a parallel video processing techniques as pipeline for analyzing image modules such as face detection, recognition and tracking so that multiple people can be identified more efficiently and smoothly with increased performance and computational efficiency. Parallel processing techniques makes the use of super resolution algorithm obsolete for major modification in generating high-resolution video images. Recognition of multiple people with super resolution can be tracked from real time live videos or it could be recorded on