Author:
Mitsuharu Matsumoto
Affiliation:
The University of Electro-Communications, Japan
Keyword(s):
Human detection, Self-quotient ε-filter, Histograms of oriented gradients, Feature extraction, Noise corrupted image.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Robotics and Automation
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
This paper describes a noise robust SVM-based human detection combining self-quotient ε-filter (SQEF) and histograms of oriented gradients (HOG). Although human detection combining HOG and SVM is a powerful approach, as it uses local intensity gradients, it is difficult to handle noise corrupted images. To handle noise corrupted images, we introduce self-quotient ε-filter (SQEF), and implement it in human detection combining HOG and SVM. SQEF is an advanced self-quotient filter (SQF), and can clearly extract features from the images not only when they have illumination variations but also when they are corrupted with noise. The new approach gives a robust human detection from noise corrupted images using the data trained by intact images without noise.