Paper Info
Title
Abnormal crowd behavior detection using novel optical flow-based features
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.
Year
DOI
Venue
2017
10.1109/AVSS.2017.8078503
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Keywords
Field
DocType
effective direction invariant event features,normal crowd behavior,normal behavior,optical flow vectors,anomaly feature,abnormal crowd behavior detection,novel optical flow,angle difference information,optical flow magnitude
Computer vision,Pattern recognition,Noise measurement,Computer science,Support vector machine,Image processing,Feature extraction,Invariant (mathematics),Artificial intelligence,Pixel,Optical flow,Crowd psychology
Conference
ISBN
Citations 
PageRank 
978-1-5386-2940-6
2
0.36
References 
Authors
21
3
Name
Order
Citations
PageRank
C. Direkoğlu18413.31
Melike Sah2417.29
Noel E. O'Connor32137223.20