Zhanpeng Shao
College of Information Science and Engineering,Hunan Normal UniversityNo.36 Lushan Road, Changsha, China 410081 |
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I am currently an Associate Professor with College of Information Science and Engineering, Hunan Normal University. I received my Ph.D. degree from Department of Mechanical and Biomedical in City University of Hong Kong in 2015, supervised under Prof. Y.F. Li. From 2018 to 2020, I was a Postdoctoral Research Fellow working with Prof. Hong Zhang in the R/V lab at University of Alberta, CA. From 2016 to 2022, I was an Associate Professor with College of Computer Science and Technology, Zhejiang University of Technology.
My current research focuses on creating algorithms that allow machines to intelligently and friendly interact with people, which can be applied to robots that work in homes, factories, and labs. I am now specifically interested in activity understanding in videos. I draw ideas from differential geometry, pattern recognition, optimization, and deep learning to develop algorithms that enable robots detect and recognize actions and activities from videos in a human-like way. I am also seeking to develop approaches in various aspects, including feature extraction, shape description, human pose estimation, and human-object interaction detection.
A Multi-level Network for Human Pose Estimation IEEE International Conference on Robotics and Automation (ICRA), 2021
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A Spatiotemporal Descriptor for Rigid Body Motion Recognition IEEE Transactions on Cybernetics (TCYB), 2018
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Vision Based Hand Gesture Recognition Using 3D Shape Context IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018
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MSM-HOG:A Flexible Trajectory Descriptor for Rigid Body Motion Recognition IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
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• We have organized a Special Issue titled "Human-Robot Interaction for Intelligent Education and Engineering Applications" on MDPI Sensors (IF=3.576). This Special Issue aims to provide an opportunity for researchers to publish their theoretical and technological studies on emerging theories in HRI-based intelligent education and their engineering applications within this domain.