Searched for: subject:"Video%5C+annotations"
(1 - 4 of 4)
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Zhang, B. (author), Wilschut, E.S. (author), Willemsen, D.M.C. (author), Martens, M.H. (author)
Automated truck platooning is getting an increasing interest for its potentially beneficial effects on fuel consumption, driver workload, traffic flow efficiency, and safety. Nevertheless, one major challenge lies in the safe and comfortable transitions of control from the automated system back to the human drivers, especially when they have...
article 2019
document
Xu, J. (author), Broekens, J. (author), Hindriks, K. (author), Neerincx, M.A. (author)
This paper reports our investigation into the effects of bodily mood expression of a humanoid robot in a scenario close to real life. To this end, we used the NAO robot to perform as a lecturer in a university class. To display either a positive or a negative mood, we modulated 41 co-verbal gestures by adjusting behavior parameters that control...
conference paper 2014
document
Xu, J. (author), Broekens, J. (author), Hindriks, K. (author), Neerincx, M.A. (author)
conference paper 2014
document
Schavemaker, J.G.M. (author), Thomas, E.D.R. (author), Havekes, A. (author)
In this contribution we present a method to classify segments of gorilla videos1 in different affective categories. The classification method is trained by crowd sourcing affective annotation. The trained classification then uses video features (computed from the video segments) to classify a new video segment into one of different affective...
conference paper 2014
Searched for: subject:"Video%5C+annotations"
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