Abstract
Human action recognition is an essential ability for service robots to interact with human users. This paper presents a novel semi-supervised learning system for a service robot to recognise human actions and provide services. The multi-class co-training algorithm is firstly adopted to leverage the information from unlabelled data using two different classifiers selected by means of the diversity measure of entropy. The confidence score is then derived from both the nearer labelled and the unlabelled neighbours. The unlabelled examples with both the higher and the lower confidence scores are added to the labelled training set. To evaluate the proposed algorithm, mixed descriptors are used to express actions so that the recognition algorithm can quickly complete the recognition process from a single frame of visual image. Experimental results are presented to show that the proposed 3D vision-based semi-supervised learning system can recognise simple human actions effectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
