An unsupervised method for anomaly detection from crowd videos

Date

2013-01-01

Author

Guler, Puren
Temizel, Alptekin
Temizel, Tugba Taskaya

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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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Citation Formats

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.