Real-time non-contact breath detection from video using adaboost and Lucas-Kanade algorithm
A simple and economical system is developed to detect human breath in a real time fashion with a normal webcam. By using the optical flow method, a novel approach is proposed to detect the peak of inspiratory phase from a series of frames that are captured from a camera to define the time for trigging X-ray shooting. The issue of using images to detect breath is that the motions of respiratory features on chest are very small, since the camera is attached on the X-ray machine with a significant distance. Therefore, the Lucas-Kanade algorithm is considered to track small motions on chest. Adaboost and corner detection are developed to identify region of chest and to obtain useful feature points for the Lucas-Kanade algorithm. From the experimental conducted, by using the proposed approach, the motion of respiratory phase is easily observed and the predictive error of the peak time is about 0.488 second, approximately 9.75% of the averaging breath cycle. It is acceptable for the X-ray shooting purpose.