Abstract
The ability to recognize human activities is necessary to facilitate natural interaction between humans and robots. While humans can distinguish between communicative actions and activities of daily living, robots cannot draw such inferences effectively. To allow intuitive human robot interaction, we propose the use of human-like stylized gestures as communicative actions and contrast them from conventional activities of daily living. We present a simple yet effective approach of modelling pose trajectories using directions traversed by human joints over the duration of an activity and represent the action as a histogram of direction vectors. The descriptor benefits from being computationally efficient as well as scale and speed invariant. In our evaluation, the descriptor returned state of the art classification accuracies using off the shelf classification algorithms on multiple datasets.
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Chrungoo, A., Manimaran, S.S., Ravindran, B. (2014). Activity Recognition for Natural Human Robot Interaction. In: Beetz, M., Johnston, B., Williams, MA. (eds) Social Robotics. ICSR 2014. Lecture Notes in Computer Science(), vol 8755. Springer, Cham. https://doi.org/10.1007/978-3-319-11973-1_9
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DOI: https://doi.org/10.1007/978-3-319-11973-1_9
Publisher Name: Springer, Cham
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