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پنجمين دوره كنفرانس بین‌المللی اينترنت اشيا و كاربردها
The 5th International Conference on Internet of Things and Its Application

:: IoT based smart surveillance monitoring by using model-based human action recognition design


IoT based smart surveillance monitoring by using model-based human action recognition design

 

Amir Reza Ebrahimy

Department of Computer

Engineering, University of Isfahan,

Isfahan, Iran

Ebrahimy@hotmail.com

Ahmad Reza Naghsh Nilchi

Department of Computer

Engineering, University of Isfahan,

Isfahan, Iran

Email: Nilchi@eng.ui.ac.ir

Amir Hassan Monadjemi

Department of Computer

Engineering, University of Isfahan,

Isfahan, Iran

monadjemi@eng.ui.ac.ir

Mohmmad Saeid Ehsani

Department of Computer Engineering,

University of Isfahan, Isfahan, Iran

Email: Ehsani@eng.ui.ac.ir

AbstractIn recent years, with the rapid development in IP camera usage, massive video surveillance data are produced at an unprecedented speed. IP camera is one of the most important elements in In Internet of things (IoT) and smart cities, in this era. IoT Based Smart Surveillance provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different applications. Traditional solutions to deal with the big video data would require a large amount of computing and storage resources.one of the major bottleneck being faced the computational cost of spatio-temporal deep neural networks making them run as fast as their 2D counterparts while preserving accuracy on video recognition benchmarks. To this end, we present the new model-based human action recognition design. In this method we use fixed dimensional representation for actions in video clips of varying lengths to decrees computational complexes. This representation, simplify clustering and comparing between videos. In addition, the methods based on CNN usually need manual annotation at the beginning and the meaning of characteristics is an abstract wonder and subjective phenomenon and hence a manual annotation of attributes is highly inconsistent [1]. Anyhow our proposed smart surveillance system is unsupervised and don't need inconsistent manual annotation of video clips attributes. Many researchers believe smart surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent surveillance systems focuses on human action Recognition.

Keywords: Human Action Recognition, Smart Surveillance System, smart cities, Unsupervised Feature Extraction, Gaussian mixture model

 






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