Action recognition technique based on fast HOG3D of integral foreground snippets and random forest

Action recognition is considered a promising field in computer vision and can be used in many applications such as video indexing and retrieval. In this paper, we present a novel technique for action recognition based on traditional three stages of feature extraction, action learning, and action recognition. The proposed technique builds a foreground snippet from the input video file, then uses integral videos representation of the foreground snippet to extract HOG3D feature vector. After that, random forest is constructed and trained from feature space to classify the Weizmann actions. Several experiments are performed to show the effectiveness, invariance and speed of the proposed technique against state of the art techniques. Experiments are made on ten different human actions on the Weizmann dataset. The best obtained average recall and average specificity values were 95.68 and 93.21, respectively.