Visual Estimation of Attentive Cues in HRI: The Case of Torso and Head Pose

Capturing visual human-centered information is a fundamental input source for effective and successful human-robot interaction (HRI) in dynamic multi-party social settings. Torso and head pose, as forms of nonverbal communication, support the derivation people’s focus of attention, a key variable in the analysis of human behaviour in HRI paradigms encompassing social aspects. Towards this goal, we have developed a model-based approach for torso and head pose estimation to overcome key limitations in free-form interaction scenarios and issues of partial intraand inter-person occlusions. The proposed approach builds up on the concept of Top View Re-projection (TVR) to uniformly treat the respective body parts, modelled as cylinders. For each body part a number of pose hypotheses is sampled from its configuration space. Each pose hypothesis is evaluated against the a scoring function and the hypothesis with the best score yields for the assumed pose and the location of the joints. A refinement step on head pose is applied based on tracking facial patch deformations to compute for the horizontal offplane rotation. The overall approach forms one of the core component of a vision system integrated in a robotic platform that supports socially appropriate, multi-party, multimodal interaction in a bartending scenario. Results in the robot’s environment during real HRI experiments with varying number of users attest for the effectiveness of our approach.