Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Chapter

Download BibTeX

Title

Human Behavior Recognition Using Negative Curvature Minima and Positive Curvature Maxima Points

Authors

[ 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 ] phd student | [ P ] employee

Year of publication

2015

Chapter type

chapter in monograph / paper

Publication language

english

Abstract

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.

Pages (from - to)

57 - 66

DOI

10.1007/978-3-319-10383-9_6

URL

https://link.springer.com/chapter/10.1007/978-3-319-10383-9_6

Book

New Research in Multimedia and Internet Systems

Presented on

9th International Conference on Multimedia and Network Information Systems, MISSI'2014, 17-19.09.2014, Wrocław, Poland

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.