A Systematic Review of Algorithms in People Images Detection Based on Artificial Vision Techniques for Energy Management in Air Conditioners
DOI:
https://doi.org/10.3991/ijoe.v17i01.17899Keywords:
energy management, detection algorithm, artificial vision, internet of things, sensors.Abstract
Over the years, the development of artificial intelligence has influenced the fact that the algorithms applied in video devices are renewed every day in their object detection area, such as pattern recognition for detecting an object, image, person. This research aims to identify the algorithms for detecting people's image through artificial vision, and its application focused on energy management in air conditioners. The following research questions were established: Q1: How many studies refer to algorithms based on detecting a person's image? Q2: How many studies refer to energy management in air conditioners based on artificial vision? Q3: Are there artificial vision techniques for the automatic turning on and off a device? Q4: What types of algorithms in detecting a person's image based on artificial vision exist today? The search for information was based on research criteria related to the topic developed in four virtual libraries. This research criterion includes 213 relevant studies, including 46 that were of interest for developing the research. This research's results determine that the viola-jones algorithm shows greater effectiveness with 91.5% in relation to the other algorithms, followed by the HOG algorithm with 90.29% effectiveness. Considering the different efficiency parameters established in this study, we can conclude that this algorithm can be used in various applications, security, energy management, video surveillance and environment.
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Published
2021-01-19
How to Cite
Santana Mantuano, E., Garcia-Quilachamin, W. X., & Anchundia Santana, J. (2021). A Systematic Review of Algorithms in People Images Detection Based on Artificial Vision Techniques for Energy Management in Air Conditioners. International Journal of Online and Biomedical Engineering (iJOE), 17(01), pp. 17–33. https://doi.org/10.3991/ijoe.v17i01.17899
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