Publication: Unattended and stolen object detection based on relocating of existing object
Issued Date
2014-01-01
Resource Type
Other identifier(s)
2-s2.0-84911421847
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of the 2014 3rd ICT International Senior Project Conference, ICT-ISPC 2014. (2014), 115-118
Suggested Citation
Waritchana Rakumthong, Natpaphat Phetcharaladakun, Wichuda Wealveerakup, Nawat Kamnoonwatana Unattended and stolen object detection based on relocating of existing object. Proceedings of the 2014 3rd ICT International Senior Project Conference, ICT-ISPC 2014. (2014), 115-118. doi:10.1109/ICT-ISPC.2014.6923231 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/33701
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Unattended and stolen object detection based on relocating of existing object
Other Contributor(s)
Abstract
© 2014 IEEE. This paper proposes a new design and implementation method in supporting a smart surveillance system that can automatically detect abandoned and stolen objects in public places such as bus stations, train stations or airports. The developing system is implemented by using image processing techniques. In the circumstance such as when suspicious events (i.e. left unattended or stolen objects) have been detected, the system will alert to people responsible for the role such as security guards or security staff. The detection process consists of four major components: 1) Video Acquisition 2) Video Processing 3) Event Detection and 4) Result Presentation. The experiment will be conducted in order to access the following qualities: 1) Usability: to verify that the system can detect the object and recognize the events, and 2) Correctness: to measure the accuracy of the system. Finally, the data sets are tested in the experimental result can measure and represent the correctness of system by percentage. The correctness of object classification is approximately 76%, and the correctness of event classification 83%.