Abstract
Nowadays, with the emergence of sophisticated electronic devices, human daily activities are becoming more and more complex. On the other hand, research has begun on the use of reliable, cost-effective sensors, patient monitoring systems, and other systems that make daily life more comfortable for the elderly. Moreover, in the field of computer vision, human action recognition (HAR) has drawn much attention as a subject of research because of its potential for numerous cost-effective applications. Although much research has investigated the use of HAR, most has dealt with simple basic actions in a simplified environment; not much work has been done in more complex, real-world environments. Therefore, a need exists for a system that can recognize complex daily activities in a variety of realistic environments. In this paper, we propose a system for recognizing such activities, in which humans interact with various objects, taking into consideration object-oriented activity information, the use of deep convolutional neural networks, and a multi-class support vector machine (multi-class SVM). The experiments are performed on a publicly available cornell activity dataset: CAD-120 which is a dataset of human–object interactions featuring ten high-level daily activities. The outcome results show that the proposed system achieves an accuracy of 93.33%, which is higher than other state-of-the-art methods, and has great potential for applications recognizing complex daily activities.
Highlights
The recognition of complex daily activities and human–object interaction plays as an important role in many applications, such as monitoring systems for the elderly, for patients, for human–robot interaction, and other video surveillance systems
We propose a system for recognizing complex human–object interaction based on usage information for the objects involved
We propose a recognition system for complex human–object interactions based on the hybrid approach of combining Deep convolutional neural networks (DCNN) and multi-class support vector machine (SVM)
Summary
The recognition of complex daily activities and human–object interaction plays as an important role in many applications, such as monitoring systems for the elderly, for patients, for human–robot interaction, and other video surveillance systems. For monitoring the elderly living independently, monitoring systems must automatically analyze daily activities and detect abnormal behavior in order to provide assistance health-care services. Research has concentrated on computer vision-based human action recognition (HAR). In this area, depth sensors have gained much attention because of their reasonable cost and adaptability to variable illumination. Depth sensors have gained much attention because of their reasonable cost and adaptability to variable illumination Depth sensors, such as Microsoft Kinect [1] and ASUS Xtion
Sign-in/Register to view highlights & summary 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.
