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An unsupervised method for anomaly detection from crowd videos
Date
2013-01-01
Author
Guler, Puren
Temizel, Alptekin
Temizel, Tugba Taskaya
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Anomaly detection from crowd videos is an issue that is becoming more important due to the difficulties in maintaining the public security in crowded places. Surveillance videos has a significant role for enabling the real time analysis of the captured events occurring in crowded places. This paper presents a method that detects anomalies in crowd in real-time using computer vision and machine learning techniques. The proposed method consists of extracting the crowd behavior properties (velocity, direction) by tracking scale invariant feature transform (SIFT) feature points and fitting the extracted behavior properties into a Gaussian Model. In this paper, only the global anomalies which occur on the overall video frame are handled. According to the test results, the method gives comparable results with the state-of-art methods and also can run in real-time. In addition, it is less complex than the compared state-of-art methods and works unsupervised.
Subject Keywords
Crowd behavior analysis
,
Video surveillance applications
,
Computer vision
URI
https://hdl.handle.net/11511/30625
DOI
https://doi.org/10.1109/siu.2013.6531292
Collections
Graduate School of Informatics, Conference / Seminar
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Crowd behavior analysis and anomaly detection in crowded environments have become more important in recent years. In the literature there are two main approaches for crowd behavior analysis based on the density of the crowd. While individual analysis is more efficient for low and medium density crowds, holistic approaches which consider the crowd as a whole are more efficient for high density crowds. Crowd behavior analysis studies can be examined in 3 categories: group behavior analysis, crowd behavior ana...
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P. Guler, A. Temizel, and T. T. Temizel, “An unsupervised method for anomaly detection from crowd videos,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30625.