Human Tracking with Statistical Shape Model and Rough Pose Estimation by Random Forest

Abstract

Human tracking in surveillance camera has been challenging task in the field of computer vision. Tracking objects have large variations such as pose, body shape, clothes and so on. Especially in parts-based methods, postural change is big problem since appearnce of human changes drastically. We deal with this problem to use statistical shape model for tracking and detection. It represents the variations of postural change and body shape with low dimensions. Our trakcing result includes more detailed the position and shape of body parts. So we recognize rough pose and body direction to analyze it. These data is useful for seculity system or marketing decision in surveillance.