Abstract
In this paper we present an instance based machine learning algorithm and system for real-time object classification and human action recognition which can help to build intelligent surveillance systems. The proposed method makes use of object silhouettes to classify objects and actions of humans present in a scene monitored by a stationary camera. An adaptive background subtract-tion model is used for object segmentation. Template matching based supervised learning method is adopted to classify objects into classes like human, human group and vehicle; and human actions into predefined classes like walking, boxing and kicking by making use of object silhouettes.
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Dedeoğlu, Y., Töreyin, B.U., Güdükbay, U., Çetin, A.E. (2006). Silhouette-Based Method for Object Classification and Human Action Recognition in Video. In: Huang, T.S., et al. Computer Vision in Human-Computer Interaction. ECCV 2006. Lecture Notes in Computer Science, vol 3979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11754336_7
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DOI: https://doi.org/10.1007/11754336_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34202-1
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