In this work we focus on the issue of improving the quality of low level 2D feature extraction for human action recognition. For instance, existing algorithms such as the Optical Flow algorithm detects noisy and irrelevant features because of its lack of ground truth data sets for complex scenes. For these features, it is difficult to extract data such as coordinate positions of the features, velocity and the direction of the moving objects, and the differential data information between different frames. Extracting such low level feature data is one of the major steps involved in video based Human action recognition. Our work proposes an extended Optical Flow algorithm focusing on human actions. This uses a Frame Jump technique along with thresholding of unwanted features to overcome the problems due to complex scenes. Frame Jump restricts to detecting only useful features by removing other noisy and irrelevant features detected by the existing Optical Flow algorithm. In addition to the above, it also elucidates the integration of the proposed technique with other feature extraction algorithms.
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