ArticleComputational intelligence and neuroscience2022
KlugOculus: A Vision-Based Intelligent Architecture for Security System.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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0 citing papers in PubMed, 4 citations in OpenAlex.
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Authors and funding
5 authors at 3 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Vision-based system has gained significant attention in detecting the abnormal activities of intruders and alerting security with the amalgamation of adaptive video analytics techniques. The implementation of this kind of system works on face recognition, where the dedicated hardware with better computation power is limited in the previous studies. In this study, vision-based intelligent architecture and systems are proposed to detect intruders through facial recognition and sensors with customized hardware. As a part of the training, each subject was trained with 6 different pictures for a total of 120 images. Facial recognition implemented with machine learning (ML) inspired support vector machine (SVM) along with a histogram of oriented gradients (HOG). During the real-time implementation, the SVM model loaded in Raspberry Pi 3 has attained 99.9% accuracy for 20 different subjects. The proposed system can provide an accuracy of 99.9% even with tilted images of the subject, so it can be adopted by the different security personnel to boost the security system for the identification of intruders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.