Efficient Human-Robot Interaction using Deep Learning with Mask R-CNN: Detection, Recognition, Tracking and Segmentation

We address social human-robot interaction problem by proposing an integration of deep neural network with mechanical robotic system to make it robust for human-robot interactive activities. Mask R-CNN, a neural network for object detection, can effectively help localize human faces which can be mani...

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Bibliographic Details
Published in2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) pp. 162 - 167
Main Authors Le, Than D., Huynh, Dang T., Pham, Huy V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2018
Subjects
deep neural network
detection
Human robot interaction
mini-parallel kinematic
tracking robotics
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Summary:We address social human-robot interaction problem by proposing an integration of deep neural network with mechanical robotic system to make it robust for human-robot interactive activities. Mask R-CNN, a neural network for object detection, can effectively help localize human faces which can be manipulated to instruct movement of the robot head. Our approach is not only suitable for detection and segmentation tasks but able to integrate as well with the mechanism of parallel mini-manipulator representing the 3D dimensions, in position and orientation of workspace. It can also solve the object segmentation problem which appears to be one of the most challenging issues in computer vision nowadays.
DOI:10.1109/ICARCV.2018.8581081