Abstract
Automated human identification by their walking behavior is a challenge attracting much interest among machine vision researchers. However, practical systems for such identification remain to be developed. In this study, a machine learning approach to understand human behavior based on motion imagery was proposed as the basis for developing pedestrian safety information systems. At the front end, image and video processing was performed to separate foreground from background images. Shape-width was then analyzed using 2D discrete wavelet transformation to extract human motion features. Finally, an adaptive boosting (AdaBoost) algorithm was performed to classify human gender and age into its class. The results demonstrated capability of the proposed systems to classify gender and age highly accurately.
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Handri, S., Nomura, S., Nakamura, K. (2009). Determination of Gender and Age Based on Pattern of Human Motion Using AdaBoost Algorithms. In: Kim, JH., et al. Advances in Robotics. FIRA 2009. Lecture Notes in Computer Science, vol 5744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03983-6_35
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DOI: https://doi.org/10.1007/978-3-642-03983-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03982-9
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