Action Recognition from Depth Video Sequences Using Microsoft Kinect

This paper provides a fast and effective method for understanding human actions. The object performs various actions such as sitting, hand-wave, theft, and walking. The movements are captured in real time using the Microsoft Kinect sensor where the recorded video is in the mode of depth. Individual frames are taken from the video of specific action and SURF characteristics are extracted based on the frames interest points. A common approach known as the Bag of features method is used for recognition purposes, which uses k-means clustering method to create a visual vocabulary by reducing the number of features by quantizing, which in turn makes the results more accurate. For the classification of the computationally efficient depth features obtained, a multi-class SVM classifier is used. The overall classification accuracy is around 97%