Artur Jordão Lima Correia
Aluno de doutorado
Artur possui graduação em Ciência da Computação pela Universidade do Oeste Paulista (2010-2013) e mestrado em Ciência da Computação pela Universidade Federal de Minas Gerais (2014-2016). Atualmente é aluno de doutorado na Universidade Federal de Minas Gerais. Atuou como pesquisador bolsista na Fundação de Desenvolvimento da Pesquisa em parceria com a SAMSUNG. Suas áreas de interesse incluem reconhecimento de padrões visuais, partial least squares e machine learning.
Dissertação de mestrado
Pedestrian detection is a well-known problem in Computer Vision, mostly because of its direct applications in surveillance, transit safety and robotics. In the past decade, several efforts have been performed to improve the detection in terms of accuracy, speed and feature enhancement. In this work, we propose and analyze techniques focusing on these points. First, we develop an accurate oblique random forest (oRF) associated with Partial Least Squares (PLS). The method utilizes the PLS to find a decision surface, at each node of a decision tree, that better splits the samples presented to it, based on some purity criterion. To measure the advantages provided by PLS on the oRF, we compare the proposed method with the oRF based on SVM. Second, we evaluate and compare filtering approaches to reduce the search space and keep only potential regions of interest to be presented to detectors, speeding up the detection process. Experimental results demonstrate that the evaluated filters are able to discard a large number of detection windows without compromising the accuracy. Finally, we propose a novel approach to extract powerful features regarding the scene. The method combines results of distinct pedestrian detectors by reinforcing the human hypothesis, whereas suppressing a significant number of false positives due to the lack of spatial consensus when multiple detectors are considered. Our proposed approach, referred to as Spatial Consensus (SC), outperforms all previously published state-of-the-art pedestrian detection methods.