Authors:
Marcos Baptista-Ríos
;
Marta Marrón-Romera
;
Cristina Losada-Gutiérrez
;
José Angel Cruz-Lozano
and
Antonio del Abril
Affiliation:
University of Alcalá, Spain
Keyword(s):
People Detector, Partial Occlusion, Histogram of Oriented Gradients (HOG), Support Vector Machine (SVM).
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
This work presents a robust system for people detection in RGB images. The proposal increases the robustness
of previous approaches against partial occlusions, and it is based on a bank of individual detectors whose
results are combined using a multimodal association algorithm. Each individual detector is trained for a
different body part (full body, half top, half bottom, half left and half right body parts). It consists of two
elements: a feature extractor that obtains a Histogram of Oriented Gradients (HOG) descriptor, and a Support
Vector Machine (SVM) for classification. Several experimental tests have been carried out in order to validate
the proposal, using INRIA and CAVIAR datasets, that have been widely used by the scientific community.
The obtained results show that the association of all the body part detections presents a better accuracy that
any of the parts individually. Regarding the body parts, the best results have been obtained for the full body
and half top body.