Anomaly Detection in Trajectories

Date

2016-05-19

Author

Ergezer, Hamza
Leblebicioğlu, Mehmet Kemal

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

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In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via covariance features enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances, anomaly detection is achieved by sparse representations on nearest neighbours. Conducted experiments on both synthetic and real datasets show that the proposed method yields results which are outperforming or comparable with state of the art.

Subject Keywords

Covariance feature, Trajectory representation, Anomaly detection

URI

https://hdl.handle.net/11511/54253

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

H. Ergezer and M. K. Leblebicioğlu, “Anomaly Detection in Trajectories,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54253.