A FRAMEWORK FOR DETECTING COMPLEX EVENTS IN SURVEILLANCE VIDEOS

Date

2013-07-19

Author

Onal, Itir
Kardas, Karani
Rezaeitabar, Yousef
Bayram, Ulya
Bal, Murat
Ulusoy, İlkay
Cicekli, Nihan Kesim

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper presents a framework for detecting complex events in surveillance videos. Moving objects in the foreground are detected in the object detection component of the system. Whether these foregrounds are human or not is decided in the object recognition component. Then each detected object is tracked and labeled in the object tracking component, in which true labeling of objects in the occlusion situation is also provided. The extracted information is fed to the event detection component. Rule based event models are created and trained using Markov Logic Networks (MLNs) so that each rule is given a weight. Events are inferred using MLNs where the assigned weights are used to determine whether an event occurs or not. The proposed system can be applied to detect many complex events simultaneously. In this paper, detection of left object event is discussed and evaluated using PETS-2006, CANTATA and our dataset.

Subject Keywords

Event detection, Markov logic networks, Video surveillance, Foreground detection, Human recognition, Tracking

URI

https://hdl.handle.net/11511/52524

DOI

https://doi.org/10.1109/ICMEW.2013.6618411

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

I. Onal et al., “A FRAMEWORK FOR DETECTING COMPLEX EVENTS IN SURVEILLANCE VIDEOS,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52524.