{"id":1140,"date":"2019-05-18T14:54:42","date_gmt":"2019-05-18T17:54:42","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1140"},"modified":"2019-05-18T14:54:42","modified_gmt":"2019-05-18T17:54:42","slug":"vol15-no1-art4","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/","title":{"rendered":"WI-FI DEVICE IDENTIFICATION IN CROWD COUNTING USING MACHINE LEARNING METHODS"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> WI-FI DEVICE IDENTIFICATION IN CROWD COUNTING USING MACHINE LEARNING METHODS<\/p>\n<p><strong>Autores:<\/strong>\u00a0Jean Pierre Jarrier Conti, Tiago Buatim Nion da Silveira, Heitor Silv\u00e9rio Lopes<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong>The increase in the availability of computational resources gave rise to new technologies to estimate the amount of people in a given area. In this context, algorithm-based solutions for crowd counting can be grouped into image-based and non-image based approaches, the latter considering any other feature that is not visual. Currently, due to the popularization of smartphones and mobile devices, several researchers have been using Wi-Fi request packets for crowd counting estimation. Assuming that, on average, each person in a given place carries a Wi-Fi device, the number of unique MAC addresses can be associated with the number of people. However, since the probe may capture all Wi-Fi traffic \u2013 which may include broadcast messages from other access points or packets from notebooks and desktop devices \u2013 some strategy must be applied in order to identify only personal mobile devices, thus improving the method accuracy. In this work, we trained classifiers to segment mobile from static devices through its Wi-Fi behavior pattern. Therefore, using data collected from different devices and in different environments, we evaluated the proposed methodology by using several machine learning algorithms. Best results were achieved with logistic regression and neural network (MLP). The results of this study suggest the feasibility of the proposed method for crowd counting in high-density Wi-Fi zones.<\/p>\n<p>Palavras-chave: Crowd counting, Machine learning, IEEE 802.11, Wi-Fi, Probe requests.<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 11<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/LNLM-vol15-no1-art4\">10.21528\/LNLM-vol15-no1-art4<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/03\/vol15-no1-art4.pdf\">vol15-no1-art4.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/03\/vol15-no1-art4.bib\">vol15-no1-art4.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: WI-FI DEVICE IDENTIFICATION IN CROWD COUNTING USING MACHINE LEARNING METHODS Autores:\u00a0Jean Pierre Jarrier Conti, Tiago Buatim Nion da Silveira, Heitor Silv\u00e9rio Lopes Resumo:The increase in the availability of computational resources gave rise to new technologies to estimate the amount <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":992,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1140","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>WI-FI DEVICE IDENTIFICATION IN CROWD COUNTING USING MACHINE LEARNING METHODS - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"WI-FI DEVICE IDENTIFICATION IN CROWD COUNTING USING MACHINE LEARNING METHODS - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: WI-FI DEVICE IDENTIFICATION IN CROWD COUNTING USING MACHINE LEARNING METHODS Autores:\u00a0Jean Pierre Jarrier Conti, Tiago Buatim Nion da Silveira, Heitor Silv\u00e9rio Lopes Resumo:The increase in the availability of computational resources gave rise to new technologies to estimate the amount Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/\",\"name\":\"WI-FI DEVICE IDENTIFICATION IN CROWD COUNTING USING MACHINE LEARNING METHODS - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"datePublished\":\"2019-05-18T17:54:42+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art4\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; 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