Anomaly Detection with Machine Learning Algorithms in Crowded Scenes in UMN Anomaly Dataset

Year 2021, Volume: 16 Issue: 1, 1 - 6, 28.01.2021

Hatice Kübra Boyrazlı Ahmet Çınar

Abstract

In recent years, keeping security under control in crowded environments has been a common problem. Camera systems are used to ensure security in crowded environments. When the video images recorded by the cameras are examined, it is checked whether there is any dangerous and unusual movement in the environment and appropriate measures are developed. Human behavior must be modelled to detect normal and abnormal behaviors in crowded scenes. In this study, crowded scenes in three different environments in the UMN Anomaly Data Set were examined. Random Forest, Support Vector Machines and k Nearest Neighbour algorithms, which are one of the machine learning methods in these three different environments, are applied. As a result of algorithms applied, the abnormal behaviour (like escape) of people in a crowded scene has been detected. Performance criteria such as accuracy, sensitivity, precision and F1 score of these applied algorithms were calculated and compared.

Keywords

Artificial Intelligence, Machine Learning, Anomaly Detection, Crowded Analyse, Algorithm

References

• Sezer, E.S. and Can, A.B., (2018). Anomaly Detection in Crowded scenes using log-euclidean covariance matrix, VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Vol:4, No:Visigrapp, 279–286.
• Cong, Y., Yuan, J., and Liu, J., (2011). Sparse Reconstruction Cost for Abnormal Event Detection, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, No. June 2014, 3449–3456.
• Wang, X., Ma, X., and Grimson, W.E.L., (2009). Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3):539–555.
• Wiliem, A., Madasu, V., Boles, W., and Yarlagadda, P., (2008). Detecting Uncommon Trajectories, Proceedings-Digital Image Computing: Techniques and Applications, DICTA 2008, No. January, 398–404.
• Popoola, O.P. and Wang, K., (2012). Video-based Abnormal Human Behavior Recognitiona Review, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 42(6):865–878.
• Zhou, B., Wang, X., Tang, X. (2012). Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2871–2878.
• Hu, M., Ali, S., and Shah, M., (2008). Learning Motion Patterns in Crowded Scenes Using Motion Flow Field, Proceedings - International Conference on Pattern Recognition, 2–6.
• Chong, Y.S. and Tay, Y.H., (2017). Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol:10262 LNCS, 189–196.
• Monitoring Human Activity, URL: http://mha.cs.umn.edu/ (Accessing Time: February, 10, 2019)
• https://veribilimcisi.com/2017/07/19/destek-vektor-makineleri-support-vector-machine/.