Visual estimation of attentive cues in HRI: The case of torso and head pose.

Sigalas M., Pateraki M. and Trahanias P., 2015. Visual estimation of attentive cues in HRI: The case of torso and head pose. In Computer Vision Systems, Lecture Notes in Computer Science, Volume 9163, pp. 375-388. Proc. of the 10th Intl. Conference on Computer Vision Systems (ICVS), 6-9 July, Kopenhagen, Denmark. [doi] [bib]


Capturing visual human-centered information is a fundamental input source for e ective 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 intra- and 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 confi guration 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 refi nement step on head pose is applied based on tracking facial patch deformations to compute for the horizontal o ff-plane 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 eff ectiveness of our approach.

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