Pose and Gaze Estimation in Multi-camera Networks for Non-restrictive HCI
Multi-camera networks offer potentials for a variety of novel human-centric applications through provisioning of rich visual information. In this paper, face orientation analysis and posture analysis are combined as components of a human-centered interface system that allows the user’s intentions and region of interest to be estimated without requiring carried or wearable sensors. In pose estimation, image observations at the cameras are first locally reduced to parametrical descriptions, and Particle Swarm Optimization (PSO) is then used for optimization of the kinematics chain of the 3D human model. In face analysis, a discrete-time linear dynamical system (LDS), based on kinematics of the head, combines the local estimates of the user’s gaze angle produced by the cameras and employs spatiotemporal filters to correct any inconsistencies. Knowing the intention and the region of interest of the user facilitates further interpretation of human behavior, which is the key to non-restrictive and intuitive human-centered interfaces. Applications in assisted living, speaker tracking, and gaming can benefit from such unobtrusive interfaces.
KeywordsParticle Swarm Optimization Posture Estimation Linear Quadratic Regulation Assisted Living Linear Dynamical System
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