Complex Human–Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach
| Autor: | Pyke Tin, Cho Nilar Phyo, Thi Thi Zin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Activities of daily living Computer science Remote patient monitoring 02 engineering and technology Machine learning computer.software_genre lcsh:Technology Convolutional neural network Field (computer science) lcsh:Chemistry 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering complex human activities recognition depth sensor General Materials Science lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes lcsh:T business.industry Process Chemistry and Technology Deep learning General Engineering deep learning Object (computer science) multi-class SVM lcsh:QC1-999 Computer Science Applications Variety (cybernetics) Support vector machine object usage probability lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 020201 artificial intelligence & image processing Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business computer lcsh:Physics |
| Zdroj: | Applied Sciences Volume 9 Issue 9 Applied Sciences, Vol 9, Iss 9, p 1869 (2019) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app9091869 |
| Popis: | 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&ndash 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. |
| Databáze: | OpenAIRE |
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