ACADSTAFF UGM
| Title | : | Unattended Object Identification for Intelligent Surveillance System Using Sequence of Dual Background Difference |
| Author | : |
Wahyono, Ph.D. (1) Alexander Filonenko (2) Prof. Kanghyun Jo (3) |
| Date | : | 0 2016 |
| Keyword | : | Cameras, Object recognition, Computational modeling, Detectors, Surveillance, Feature extraction, Informatics Cameras, Object recognition, Computational modeling, Detectors, Surveillance, Feature extraction, Informatics |
| Abstract | : | mage-based surveillance systems are widely employed toward safety and security applications in many fields. Cameras, that are connected over an IP network for monitoring public areas, can produce large quantities of video footage. It is tedious for humans to simultaneously observe every type of event on several cameras. Thus, it is necessary to build a userfriendly intelligent system, enabling the analysis of video to detect suspicious events. One of the most important tasks of this system would be to identify unattended objects to prevent an unexpected accident such as the bombing of a public space. This paper presents a novel technique for such a task. The method is based on a sequence of dual background differences, which is obtained by computing the intensity difference between the current and reference background models within a time period. A clustering and object detector are then integrated to identify the unattended objects. The effectiveness of method was verified using public and our own databases. The results confirmed that the method is efficient to detect unattended objects and is suitable for implementation in video surveillance systems. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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