Kitchengam’ Criteria on the Use of Algorithms in a Person’s Pattern Detection, which Contribute to Safety, Surveillance and Energy Efficiency: Study of Art

Washington Garcia-Quilachamin, Julieta Evangelina Sánchez - Cano, Luzmila Pro Concepción


Among the technological evolution is the application of algorithms in cameras for the detection and recognition of people, being a contribution to the security and surveillance in commercial, home areas, and smart cities. The objective of this research is to know and identify algorithms in the detection of patterns of a person, considering the criteria of Kitchengam. For this purpose, the following research questions were asked: Q1) How many studies refer to algorithms in pattern recognition? Q2: What types of algorithm models exist in an environment related to pattern recognition? and Q3: What types of pattern recognition algorithms currently exist? The search process was carried out in the digital libraries IEEE Xplore, ACM Digital Library, Springer Link and Science Direct (Elsevier). Obtained 1402 potentially eligible studies and obtained a final sample of 28 papers considered as main research studies. The results obtained allow us to consider the Support Vector Machines model with 92% recognition and the Viola-Jones algorithm with effective detection of 97,53%, are a contribution to the surveillance and safety of people within the recognition and detection of a person’s pattern, considering also as a challenge its feasibility focused on energy efficiency, in domestic, business and smart cities.


Algorithms in detection; algorithm models; systematic review; viola-jones; Kitchengam criteria; energy efficiency

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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