In this paper, we propose an economical system for remote video player control. Through this system, we can use several simple gestures to control the video player, and these gestures can be alternated based on the user’s requirement and habits. The datasets used to train the gesture recognition model are recorded by a simple web camera in the laboratory. We utilize the CNN (convolutional neural network) to train the datasets and the user interface is designed by PyQt5. The gesture recognition system can be applied to switching television programs, controlling video games and household appliances etc.
With the development of collaborative robots, robot programming by demonstration (PBD) plays an important role in human-robot interaction, it aims to transfer new skills from observations of tasks demonstrated by humans to robots. In this paper, we proposed a new approach to teach a robot to draw pictures based on human fingertip recognition and hand motion tracking. Combining the robot operating system (ROS), OpenCV and Moveit (motion planning libraries), we capture the finger movement trajectory by using Kinect2 depth camera. Then the trajectory waypoint is sent to the ur5 robotic arm through topic communication to complete the trajectory tracking task. The experiment indicates that the proposed approach allows inexperienced users to efficiently teach a robot to track the demonstrated trajectory.
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