Author:
Mitsuharu Matsumoto
Affiliation:
The University of Electro-Communications, Japan
Keyword(s):
Parameter setting, Nonlinear filter, Support vector machine, Self-quotient ɛ-filter, Human detection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Vision and Perception
Abstract:
This paper describes SVM-based parameter setting of self-quotient ɛ-filter (SQEF), and its application to noise robust human detection combining SQEF, histograms of oriented gradients (HOG), and support vector machine (SVM). 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. On the other hand, although human detection combining SQEF, HOG and SVM can realize noise robust human detection, SQEF requires manual parameter setting. Our aim is not only to train SVM but also to adjust the parameter of self-quotient ɛ-filter using the trained SVM in training procedure. The experimental results show that we can realize noise robust human detection by using SQEF with the obtained parameter, HOG and SVM trained by intact images without noise.