Louis-Philippe Morency: “Real-time Head Pose Estimation Using a Webcam”

December 4, 2008 | Orlando, FL

Speaker: Louis-Philippe Morency
Host: 26th Army Science Conference

Accurately estimating the person’s head position and orientation is an important task for a wide range of applications such as driver awareness and human-robot interaction. Over the past two decades, many approaches have been suggested to solve this problem, each with its own advantages and disadvantages. In this paper, we present a probabilistic framework called Monocular Adaptive View-based Appearance Model (MAVAM) which integrates the advantages from two of these approaches: (1) the relative precision and user-independence of differential registration, and (2) the robustness and bounded drift of keyframe tracking. In our experiments, we show how the MAVAM model can be used to estimate head position and orientation in real-time using a simple monocular camera. Our experiments on two previously published datasets show that the MAVAM framework can accurately track for a long period of time (>2 minutes) with an average accuracy of 3.9 degrees and 1.2in with an inertial sensor and a 3D magnetic sensor.