A simple sequential pose recognition model for sleep apnea

Many existing approaches in computer vision to pose estimation make simplifications of the measurement problem, either using silhouettes or assuming knowledge of appearance or color. However, recognizing the pose of a person who is persistently under cover remains challenging. We present a real time monocular-video approach for markerless pose estimation of human body under cover without manual initialization. In order to deal with heavy occlusion, we propose a model that reinforces both feature space and model parameters by adjacent parameters and a novel search framework that aggregates detections over time to produce a more reliable hypothesis. In addition, we have introduced a novel head model, which has the combined effect of improving performance and increasing efficiency. Furthermore, we have proposed a novel representation to estimate upper leg posture using latent features. In evaluation, we demonstrate the techniques to estimate the covered body pose with various postures and obscuration levels in two environmental settings.