Affiliations: [a] School of Computer Science, Baoji University of Arts and Sciences, Baoji, China
| [b] Faculty of Computing, IBM CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia
| [c] Institute of Research and Development, Duy Tan University, Da Nang, Vietnam | [d] School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| [e] Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Malaysia
Correspondence:
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Address for correspondence: Sinan Q. Salih, Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam. E-mail: [email protected].
Abstract: BACKGROUND:Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease. OBJECTIVES:This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process. RESULTS:The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset. CONCLUSION:The results are compared with the existing method for metrics accuracy, classification time, and recall.
