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The International Conference on Artificial Intelligence and Computer Vision

AICV 2020: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) pp 258–267Cite as

Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

In the last few years, surveillance cameras have been massively distributed both indoors and outdoors in public, due to security concerns creating a need to monitor unexpected actions or activities in a scene. An intelligent automated approach is highly required to detect anomalies from the scene as well, to save the time and cost required by laborers to detect the anomalies manually from monitor screens. In this research, we propose a deep learning-based approach to detect abnormal events and behavior from surveillance videos in crowd scenes. Thus, by using the keyframe extractor to localize the keyframes that contain important information from video frames. The selected keyframes are used to compute the optical flow values of magnitudes, orientations, and velocities of each keyframe to generate several 2D templates. Then, the obtained 2D templates are supplied to a pre-trained model ‘AlexNet’ to extract high-level features. The Neighborhood Component Analysis (NCA) feature selection method is applied to select the appropriate features, then use these features to generate a classification model via Support Vector Machine (SVM) classifier. Results are evaluated on several public datasets, along with a new dataset that we built it that contains different videos covering abnormal events and behaviors. The obtained results proved that the proposed method outperforms other methods.

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Author information

Authors and Affiliations

  1. Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

    Alaa Atallah Almazroey & Salma Kammoun Jarraya

  2. MIR@CL Laboratory, Sfax University, Sfax, Tunisia

    Salma Kammoun Jarraya

Authors
  1. Alaa Atallah Almazroey
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  2. Salma Kammoun Jarraya
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Corresponding author

Correspondence to Alaa Atallah Almazroey .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Almazroey, A.A., Jarraya, S.K. (2020). Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_25

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