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Abnormal crowd behavior detection using novel optical flow-based features

Direkoglu, Cem, Sah, Melike and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2017) Abnormal crowd behavior detection using novel optical flow-based features. In: International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), in conjunction with IEEE AVSS 2017,, 29 Aug 2017, Lecce, Italy.

Abstract
In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample deviates significantly from the normal behavior, it is detected as abnormal crowd behavior. Although there are many optical flow based features for crowd behaviour analysis, this is the first time the angle difference between optical flow vectors in the current frame and in the previous frame is considered as a anomaly feature. Evaluations on UMN and PETS2009 datasets show that the proposed method performs competitive results compared to the state-of-the-art methods.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Image processing
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Copyright Information:©2017 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, Middle East Technical University – Northern Cyprus Campus, Scientific Research Project (SRP) Fund (Grant no: FEN-16-YG-10)
ID Code:21880
Deposited On:25 Aug 2017 10:46 by Noel Edward O'connor . Last Modified 23 Oct 2019 15:41
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