Anomaly Detection in Crowds by Fusion of Novel Feature Descriptors

Alzahrani, Abdullah J.; Khan, Sultan Daud; Ullah, Habib

:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8

FEATURE PEER-REVIEWED ARTICLE

Vol.11(16) (2020)

Anomaly Detection in Crowds by Fusion of Novel Feature Descriptors

Abdullah J. Alzahrani (College of Computer Science and Software Engineering, University of Hail, SAUDI ARABIA),
Sultan Daud Khan (Department of Computer Science, National University of Technology, PAKISTAN),
Habib Ullah (College of Computer Science and Software Engineering, University of Hail, SAUDI ARABIA).

Disciplinary: Computer and Information Sciences.

DOI: 10.14456/ITJEMAST.2020.311

Keywords: Crowd analysis; Crowd behavior; Supervised anomaly detection; Trajectories extraction; Anomalous event; Anomalous trajectory; Feature fusion; Motion feature; Clustering algorithm.

Abstract

Anomaly detection in high-density crowds is considered as an important research problem. Detecting anomalous crowd behavior is a complex problem due to unpredictable human behaviors and complex interactions of individuals in groups. In this paper, we present a supervised approach to detect anomalous trajectories. The proposed method has four subsequent steps. In the first step, we extract trajectories from the input video sequence. In the second step, we compute novel features from these trajectories. In the third step, we classify each trajectory into two classes, i.e., anomalous and normal. In the fourth step, we employ a clustering algorithm to cluster all anomalous trajectories. The resultant cluster indicates the anomalous regions in the scene. We evaluated the proposed approach on two publicly available benchmark datasets. From experimental results, we demonstrate the proposed method outperforms other state-of-the-art methods.
Paper ID: 11A16B

Cite this article:

Alzahrani, A.J., Khan, S.D. and Ullah, H.(2020). Anomaly Detection in Crowds by Fusion of Novel Feature Descriptors. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(16), 11A16B, 1-10. http://doi.org/10.14456/ITJEMAST.2020.311

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