Autonomous Machine Learning Framework for Detecting People Aliveness
| Autor: | Soo Dong Kim, Myeong Ho Song |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Continuous optimization
business.industry Process (engineering) Computer science 020206 networking & telecommunications System safety 02 engineering and technology computer.software_genre Machine learning ComputingMilieux_GENERAL Software framework Software Server 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business Set (psychology) computer |
| Zdroj: | SAS |
| DOI: | 10.1109/sas.2019.8706058 |
| Popis: | Determining the aliveness of people is essential for various people safety systems. While the accuracy of the aliveness determination is a key concern, there exist technical challenges in the aliveness determination with high accuracy. Our approach to the challenges is to incorporate a set of effective design tactics into a software framework. We employ machine learning models in the detection process and apply the continuous optimization of the aliveness model using autonomous computing principles. This paper presents a comprehensive framework which consists of design and implementation. Our extensive experiments show that the accuracy of aliveness determination with this framework outperforms at least 30% of conventional approaches. |
| Databáze: | OpenAIRE |
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