Human Behavior Recognition Using Negative Curvature Minima and Positive Curvature Maxima Points
[ 1 ] Dziekanat Wydziału Elektroniki i Telekomunikacji, Wydział Elektroniki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Katedra Telekomunikacji Multimedialnej i Mikroelektroniki, Wydział Elektroniki i Telekomunikacji, Politechnika Poznańska | [ D ] doktorant | [ P ] pracownik
2015
rozdział w monografii naukowej / referat
angielski
EN Recently, automated human behavior recognition are studied in the context of many new applications such as content-based video annotation and retrieval, highlight extraction, video summarization and video surveillance. In this chapter a novel description of human pose - a combination of negative curvature minima (NCM) and positive curvature maxima (PCM) points are proposed. Experimental results are provided in the chapter in order to demonstrate precision of the human activity recognition versus size of the descriptor (a temporal interval durations between the nodes of the model). The experimental results are focused on recognition of call for help behavior. The results prove high score of recognition of the proposed method.
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