{'fraction_non_alphanumeric': 0.016724336793540944, 'fraction_numerical': 0.0, 'mean_word_length': 4.962199312714777, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An optical flow based approach for action recognition

A new approach for motion-based representation on the basis of optical flow analysis and random sample consensus (RANSAC) method is proposed in this paper. Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene. It is intuitive that an action can be characterized by the frequent movement of the optical flow points or interest points at different areas of the human figure. Additionally, RANSAC, an iterative method to estimate parameters of a mathematical model from a set of observed data which contains inliers and outliers, can be used to filter out any unwanted interested points all around the scene and keep only those which are related to the particular human's motion. By this manner, the area of the human body within the frame is estimated and this rectangular area is segmented into a number of smaller regions or blocks. The percentage of change of interest points in each block from frame to frame is then recorded. Similar procedure is repeated for different persons performing the same action and the corresponding values are averaged for respective blocks. A matrix constructed by this strategy is used as a feature vector for that particular action. Afterwards, for the purpose of recognition using the extracted feature vectors, a distance-based similarity measure and a support vector machine (SVM)-based classification technique have been exploited. From extensive experimentations upon a standard motion database, it is found that the proposed method offers not only a very high degree of accuracy but also computational savings.