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.