Artur Jordão Lima Correia

Ph.D. Student

Artur has a degree in Computer Science from Universidade do Oeste Paulista (2010-2013) and a Master’s Degree in Computer Science from the Federal University of Minas Gerais (2014-2016). He is currently a doctoral student at the Federal University of Minas Gerais. He served as a research fellow at the Research Development Foundation in partnership with SAMSUNG. His areas of interest include visual pattern recognition, partial least squares and machine learning.

Master’s Thesis

Artur Jordao: The Good, the Fast and the Better Pedestrian Detector. Federal University of Minas Gerais, 2016.

Abstract

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.

Publications

11 entries « 1 of 2 »

Jordao, Artur; Kloss, Ricardo; Yamada, Fernando; Schwartz, William Robson

Pruning Deep Networks using Partial Least Squares Inproceedings

British Machine Vision Conference (BMVC) Workshops, pp. 1-9, 2019.

Links | BibTeX

Kloss, Ricardo Barbosa; Jordao, Artur; Schwartz, William Robson

Face Verification Strategies for Employing Deep Models Inproceedings

13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 258-262, 2018.

Links | BibTeX

Jordao, Artur; Junior, Antonio Carlos Nazare; Sena, Jessica; Schwartz, William Robson

Human Activity Recognition based on Wearable Sensor Data: A Benchmark Journal Article

arXiv, pp. 1-12, 2018.

Links | BibTeX

Jordao, Artur; Kloss, Ricardo Barbosa; Schwartz, William Robson

Latent hypernet: Exploring all Layers from Convolutional Neural Networks Inproceedings

IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2018.

Links | BibTeX

Jordao, Artur; Torres, Leonardo Antônio Borges; Schwartz, William Robson

Novel Approaches to Human Activity Recognition based on Accelerometer Data Journal Article

12 (7), pp. 1387–1394, 2018.

Links | BibTeX

Kloss, Ricardo Barbosa; Jordao, Artur; Schwartz, William Robson

Boosted Projection An Ensemble of Transformation Models Inproceedings

22nd Iberoamerican Congress on Pattern Recognition (CIARP), pp. 331-338, 2018.

Links | BibTeX

Jordao, Artur; Kloss, Ricardo; Yamada, Fernando; Schwartz, William Robson

Pruning Deep Neural Networks using Partial Least Squares Journal Article

ArXiv e-prints, 2018.

Links | BibTeX

Jordao, Artur; Sena, Jessica; Schwartz, William Robson

A Late Fusion Approach to Combine Multiple Pedestrian Detectors Inproceedings

IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016.

Links | BibTeX

Jordao, Artur; Schwartz, William Robson

Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection Inproceedings

IEEE International Conference on Image Processing (ICIP), pp. 2931-2935, 2016.

Links | BibTeX

Jordao, Artur

The Good, the Fast and the Better Pedestrian Detector Masters Thesis

Federal University of Minas Gerais, 2016.

Abstract | Links | BibTeX

11 entries « 1 of 2 »