{"id":978,"date":"2017-06-07T11:21:17","date_gmt":"2017-06-07T14:21:17","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=978"},"modified":"2017-06-07T11:21:17","modified_gmt":"2017-06-07T14:21:17","slug":"vol14-no2-art1","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol14-no2\/vol14-no2-art1\/","title":{"rendered":"PARTICLE FILTER AND VISUAL TRACKING: A HYBRID RESAMPLING APPROACH TO IMPROVING ROBUSTNESS IN CLUTTERED AND OCCLUDED ENVIRONMENTS"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> PARTICLE FILTER AND VISUAL TRACKING: A HYBRID RESAMPLING APPROACH TO IMPROVING ROBUSTNESS IN CLUTTERED AND OCCLUDED ENVIRONMENTS<\/p>\n<p><strong>Autores:<\/strong> Cordoba, Diego A. L.; Koike, Carla M. C. C.; Vidal, Flavio de Barros<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> Occlusions and cluttered environments represent real challenges for visual tracking methods. In order to increase robustness in such situations this article presents a method for visual tracking using a Particle Filter with Hybrid Resampling. Our approach consists of using a particle filter to estimate the state of the tracked object, and both particles\u2019 inertia and update information are used in the resampling stage. The proposed method is tested using a public benchmark and the results are compared with other tracking algorithms. The results show that our approach performs better in cluttered environments, as well as in situations with total or partial occlusions.<\/p>\n<p><strong>Palavras-chave:<\/strong> Visual tracking; Particle Filter; Hybrid resampling; Occlusions; Cluttered environments<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 12<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/LNLM-vol14-no2-art1\">10.21528\/LNLM-vol14-no2-art1<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2017\/04\/vol14-no2-art1.pdf\" rel=\"\">vol14-no2-art1.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2017\/04\/vol14-no2-art1.bib\" rel=\"\">vol14-no2-art1.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: PARTICLE FILTER AND VISUAL TRACKING: A HYBRID RESAMPLING APPROACH TO IMPROVING ROBUSTNESS IN CLUTTERED AND OCCLUDED ENVIRONMENTS Autores: Cordoba, Diego A. L.; Koike, Carla M. C. C.; Vidal, Flavio de Barros Resumo: Occlusions and cluttered environments represent real challenges <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol14-no2\/vol14-no2-art1\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":929,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-978","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>PARTICLE FILTER AND VISUAL TRACKING: A HYBRID RESAMPLING APPROACH TO IMPROVING ROBUSTNESS IN CLUTTERED AND OCCLUDED ENVIRONMENTS - 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\/vol14-no2\/vol14-no2-art1\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PARTICLE FILTER AND VISUAL TRACKING: A HYBRID RESAMPLING APPROACH TO IMPROVING ROBUSTNESS IN CLUTTERED AND OCCLUDED ENVIRONMENTS - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: PARTICLE FILTER AND VISUAL TRACKING: A HYBRID RESAMPLING APPROACH TO IMPROVING ROBUSTNESS IN CLUTTERED AND OCCLUDED ENVIRONMENTS Autores: Cordoba, Diego A. 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