A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait analysis for human identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
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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.