Understanding the state of the Art in Animal detection and classification using computer vision technologies

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Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorFerrante, Gabriel S.-
Autor(es): dc.creatorRodrigues, Felipe M.-
Autor(es): dc.creatorAndrade, Fernando R. H.-
Autor(es): dc.creatorGoularte, Rudinei-
Autor(es): dc.creatorMeneguette, Rodolfo-
Autor(es): dc.creatorChen, Y.-
Autor(es): dc.creatorLudwig, H.-
Autor(es): dc.creatorTu, Y.-
Autor(es): dc.creatorFayyad, U.-
Autor(es): dc.creatorZhu, X-
Autor(es): dc.creatorHu, X-
Autor(es): dc.creatorByna, S.-
Autor(es): dc.creatorLiu, X-
Autor(es): dc.creatorZhang, J.-
Autor(es): dc.creatorPan, S.-
Autor(es): dc.creatorPapalexakis, V-
Autor(es): dc.creatorWang, J.-
Autor(es): dc.creatorCuzzocrea, A.-
Autor(es): dc.creatorOrdonez, C.-
Data de aceite: dc.date.accessioned2025-08-21T20:18:37Z-
Data de disponibilização: dc.date.available2025-08-21T20:18:37Z-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/BigData52589.2021.9672049-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/237570-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/237570-
Descrição: dc.descriptionThis work presents the results of a survey through the analysis of studies published between January 2017 and May 2021, aiming to compose a broader view of the state of the art in the field of animal detection and classification using computer vision technologies in urban environments, and also the majors researches gaps available to address. We conducted an automatic search through two digital knowledge bases identifying 146 studies in the subject, among them 20 were selected for our analysis and data extraction. Further, the 20 studies were classified into 6 categories: (i) studies using SVM, (ii) studies using HOG, (iii) studies using SIFT, (iv) studies using PCA, (v) studies using CNN, and (vi) DFDL. As a result, it can be noted that the use of CNN is predominant concerning other approaches and that there are also combinations to improve the accuracy of classification models. In conclusion, it is possible to observe that the state-of-the-art approaches have been used in different situations, however, in the context of animal detection and classification in intelligent urban environments, there is still a lack of specific architectures to improve results.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionUniv Sao Paulo, Inst Sci Math & Comp Sci, Sao Carlos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Inst Biosci Letters & Exact Sci, Sao Paulo, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Inst Biosci Letters & Exact Sci, Sao Paulo, Brazil-
Descrição: dc.descriptionCNPq: 407248/2018-8-
Descrição: dc.descriptionCNPq: 309822/2018-1-
Descrição: dc.descriptionFAPESP: 2020/07162-0-
Formato: dc.format3056-3065-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2021 Ieee International Conference On Big Data (big Data)-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectSurvey-
Palavras-chave: dc.subjectAnimal-
Palavras-chave: dc.subjectComputer vision-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectDetection-
Título: dc.titleUnderstanding the state of the Art in Animal detection and classification using computer vision technologies-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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