Authors:
Alexandros Gavriilidis
;
Carsten Stahlschmidt
;
Jörg Velten
and
Anton Kummert
Affiliation:
University of Wuppertal, Germany
Keyword(s):
Human Detection, Depth Image Features, LDA Feature Combination, Video Processing.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
Visual object detection is an important task for many research areas like driver assistance systems (DASs), industrial automation and various safety applications with human interaction. Since detection of pedestrians is a growing research area, different kinds of visual methods and sensors have been introduced to overcome this problem. This paper introduces new relational depth similarity features (RDSF) for the pedestrian detection using a Time-of-Flight (ToF) camera sensor. The new features are based on mean, variance, skewness and kurtosis values of local regions inside the depth image generated by the Time-of-Flight sensor. An evaluation between these new features, already existing relational depth similarity features using depth histograms of local regions and the well known histogram of oriented gradients (HOGs), which deliver very good results in the topic of pedestrian detection, will be presented. To incorporate more dimensional feature spaces, an
existing AdaBoost algorithm
, which uses linear discriminant analysis (LDA) for feature space reduction and new combination of already extracted features in the training procedure, will be presented too.
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