Abstract
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.
Highlights
The use of technologies associated with energy efficiency in terms of video surveillance systems is gaining ground in our environment
The following research questions were developed: Q1: How many studies refer to algorithms in pattern recognition? Q2: What types of algorithm models exist in an environment related to pattern recognition? Q3:What types of pattern recognition algorithms currently exist?
Results found regarding Q2, on the types of algorithm models that exist in an environment related to pattern recognition
Summary
The use of technologies associated with energy efficiency in terms of video surveillance systems is gaining ground in our environment. Actions is a topic studied in computer vision and has applications related to video surveillance, human-to-computer iteration and security videos, despite great research, this is far from being a problem solved. It is a topic that is considered for implementation in smart cities, offices, homes and companies, the authors [2] state that human silhouette recognition systems can be connected to the internet of things (IoT). Among the difficulties with the recognition of a silhouette (occlusions, selfobstructions, variability of visual appearance, unpredictable temporal behavior, etc.) is the tracking of a variable and unknown number of objects that makes the problem more challenging, so there are reasons like observations to resolve detection errors [3]
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