Head Orientation Estimation using Particle Filtering in Multiview Scenarios
Cristian Canton-Ferrer, Josep R. Casas, Montse Pardàs · 2008
Abstract. This paper presents a novel approach to the problem of determining head pose estimation and face 3D orientation of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited and the head in the scene is approximated by an ellipsoid. Skin patches from each detected head are located in each camera view. Data fusion is performed by back-projecting skin patches from single images onto the estimated 3D head model, thus providing a synthetic reconstruction of the head appearance. A particle filter is employed to perform the estimation of the head pan angle of the person under study. A likelihood function based on the face appearance is introduced. Experimental results proving the effectiveness of the proposed algorithm are provided for the SmartRoom scenario of the CLEAR Evaluation 2007 Head Orientation dataset. 1 Video Head Pose Estimation This section presents a new approach to multi-camera head pose estimation from low-resolution images based on Particle Filtering (PF) [1]. A spatial and color analysis of these input images is performed and redundancy among cameras is exploited to produce a synthetic reconstruction of the head of the person. This informationis used to construct the likelihood function that will weight the particles of this PF based on visual information. The estimation of the head orientation will be computed as the expectation of the pan angle thus producing a real valued output. For a given frame in the video sequence, a set of N images are obtained from the N cameras. Each camera is modeled using a pinhole camera model based on perspective projection. Accurate calibration information is available. Bounding boxes describing the head of a person in multiple views are used to segment the interest area where the colour module will be applied. Center and size of the bounding box allow defining an ellipsoid model H = {c,R,s} where c is the center, R the rotation along each axis centered on c and s the length of each axis. Colour information is processed as described in the following subsection.