Automatic video analysis and motion estimation for physical activity classification

This paper presents an automatic video analysis method for physical activity classification and measurement. A wearable device is used to capture daily life data for health monitoring. Physical activity is analyzed by using the change of surrounding scenes resulting from the motion of the wearer. Recognition of different physical activities is achieved by analyzing motion characteristics in images evaluated from a set of representative pixel pairs extracted from adjacent video frames. Ambiguous and incorrect pixel pairs are removed under the epipolar constraint from stereo images. The effectiveness of the new method is demonstrated through experiments.