@article{lopez_de_celis_evaluation_nodate,
title = {An evaluation of Cascade Object Detector and Support Vector Machine methods for People Detection using a RGB-Depth camera located in a zenithal position},
author = {Eduardo López de Celis and Óscar García-Olalla Olivera and Maite García-Ordás and Enrique Alegre},
url = {https://www.ehu.eus/documents/3444171/4484752/61.pdf},
abstract = {This project solves the problem of people detection
using an RGB-Depth camera from a zenithal
position. The detection process has been
implemented for binary (head – no head) and
multiclass approaches (short hair head, long hair
head, ponytail and shoulders-no head). For this task,
Histogram of Oriented Gradients (HOG)
demonstrates to be a better feature descriptor than
Local Binary Patterns (LBP). In the classification
step, two models have been evaluated: SVM and
Cascade Object Detector. Our experiments shown
the better performance of SVM.},
keywords = {Cascade Object Detector, head detection, Histogram of Oriented Gradients, Local Binary Patterns, support vector machine},
pubstate = {published},
tppubtype = {article}
}
This project solves the problem of people detection
using an RGB-Depth camera from a zenithal
position. The detection process has been
implemented for binary (head – no head) and
multiclass approaches (short hair head, long hair
head, ponytail and shoulders-no head). For this task,
Histogram of Oriented Gradients (HOG)
demonstrates to be a better feature descriptor than
Local Binary Patterns (LBP). In the classification
step, two models have been evaluated: SVM and
Cascade Object Detector. Our experiments shown
the better performance of SVM.