Indirect human activity recognition based on optical flow method

A new method to recognize human activity with videos from a wearable camera is proposed in this paper. With a camera mounted to a human body, the moving subject won't appear in the video when the person is in some motions. But we can estimate the activity from the changes of scenes in videos. Optical flow method is a common method to calculate motion vectors of objects in two adjacent images. For higher precision, in this paper, we use Lucas-Kanade optical flow method with pyramid structure to calculate the optical flow of scenes which can reflect people's motion to some extents. When key information is extracted from the optical flow field, we design a feature descriptor to describe the motion in frames in a video. The feature descriptor contains angels, bounce information and other important information which can distinguish different motion. After getting feature descriptors, we use support vector machine to classify different motions with a machine learning method. Experimental results show that our method successfully identifies motion such as walking, running, going upstairs and going downstairs. Compared with methods based on blocking-matching, this method has fewer costs and has higher precision.