Description

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 and 2D fast Fourier transformation to extract human motion features. Finally, an adaptive boosting (AdaBoost) algorithm was performed to classify human gender and age into its class based on spatiotemporal information. The results demonstrated the capability of the proposed systems to classify gender and age highly accurately.