Abstract
In this paper, an adaptive object learning method based on deep neural network is developed for a robot to learn features of moving objects, e.g., humans and vehicles, via observation. The proposed method provides a solution for the robot to learn unknown moving objects in a real-time scenario. A hybrid scheme of learning and identification is proposed to recognize the moving object by fusion of foreground segmentation and identification.
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Xiao, S., Ge, S.S., Yan, S. (2014). Adaptive Object Learning for Robot Carinet. 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_39
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DOI: https://doi.org/10.1007/978-3-319-11973-1_39
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