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<div><p class="SPabstract">Em estudos oceanográficos é comum o uso de diferentes bases de dados visando correlacionar estruturas comuns que evidenciem ou comprovem determinado aspecto. Tal como dados oceanográficos e meteorológicos provenientes de satélites, boias, entre outros, disponibilizados publicamente por órgãos internacionais de pesquisa. O intuito deste trabalho foi desenvolver um processo de extração de conhecimento de forma a correlacionar dados de sensoriamento remoto como temperatura do oceano e níveis de clorofila-a, com dados pontuais de captura provenientes de empresas de pesca. Desta forma, a abundância do recurso vivo Bonito-Listrado (<em>Katsuwonus Pelamis</em>) foi correlacionada com parâmetros ambientais provenientes de sensores satelitais.</p></div>

Abstract-Markerless human motion tracking can be employed in many applications such as automatic surveillance, motion capture, human-machine interface and activity recognition. This problem has been extensively studied in the computer vision research community in the last years. In this context, the present paper presents an approach for 3D markerless human motion tracking based on a skeletal kinematic model of the human body. This method is applied over a 3D probabilistic occupancy grid of the environment, which is constructed by means of a Bayesian fusion of images from multiple synchronized sensoring cameras. Although the use of kinematic models in 3D human tracking is widely employed, its use over 3D probabilistic occupancy grids still was not vastly investigated in the literature. The experiments were performed using a public dataset with video sequences of people in motion. The results show that the method is capable of dealing adequately with the 3D markerless human motion tracking problem.Index Terms-skeletal kinematic model, 3D probabilistic occupancy grid, 3D markerless human tracking.