SavedAdd your journal

Journals / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2021 / Cilt: 12 - Sayı: 2

Anomaly Detection in Crowded Scenes With Machine Learning Algorithms

Makine Öğrenmesi Algoritmalarıyla Kalabalık Ortamlarda Anomali Tespiti

Hatice Kübra BOYRAZLI
Ahmet ÇINAR
Pages
229–236
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The algorithms used were compared with performance criteria such as accuracy, sensitivity, precision and F1 score.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Camera systems have a very important place as they are widely used in providing security in crowded environments.
📊
Key Findings & Data:The video images recorded by cameras are examined to check whether there is dangerous or unusual behavior.
💡
Conclusion & Impact:The algorithms used were compared with performance criteria such as accuracy, sensitivity, precision and F1 score.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Makine Öğrenmesi · Anomali Tespiti · Kalabalık Analizi

Abstract

Camera systems have a very important place as they are widely used in providing security in crowded environments. The video images recorded by cameras are examined to check whether there is dangerous or unusual behavior. It is tried to develop appropriate measures according to the result of this control. Modeling human behaviors for the definition and detection of abnormal behavior has become a popular research area in recent years. This study was carried out by applying supervised learning algorithms, one of the machine learning methods, on five different scenes that in two open data sets. Normal and abnormal motion scenes were detected on the videos in the data sets. In these two data sets, abnormal motion was detected in a total of five different locations. Random Forest, Support Vector Machines and k Nearest Neighbor algorithms, which are among the supervised learning algorithms, were used in this process. The algorithms used were compared with performance criteria such as accuracy, sensitivity, precision and F1 score.

Özet

Kamera sistemleri , kalabalık ortamlarda güvenliği sağlamada yaygın olarak kullanıldığı için oldukça önemli bir yere sahiptir. Kameralar aracılığıyla kaydedilen video görüntüleri incelenerek tehlikeli veya normalin dışında gerçekleşen davranış olup olmadığının kontrolü yapılmaktadır. Bu kontrolün sonucuna göre uygun tedbirler geliştirilmeye çalışılmaktadır. Anormal davranışların tanım ve tespiti için insan davranışlarının modellenmesi son yıllarda popüler bir araştırma alanı haline gelmiştir. Bu çalışma erişime açık iki adet veri setinde bulunan beş ayrı ortam videosu üzerinde üzerinde makine öğrenmesi yöntemlerinden biri olan denetimli öğrenme algoritmaları uygulanara ile gerçekleştirilmiştir. Veri setlerinde yer alan videolar üzerinde normal ve anormal hareket sahnelerinin tespiti yapılmıştır. Bu iki veri setinde toplam beş farklı mekanda anormal hareket tespiti yapılmıştır. Bu işlemde denetimli öğrenme algoritmalarından olan Rastgele Orman, Destek Vektör Makineleri ve k En Yakın Komşu algoritmaları kullanılmıştır. Kullanılan algoritmalar, doğruluk, duyarlılık, kesinlik ve F1 skor gibi performans ölçütleri ile karşılaştırılmıştır.

🔖

Cite This Article

BOYRAZLI, H. K., ÇINAR, A. (2022). Anomaly Detection in Crowded Scenes With Machine Learning Algorithms. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, Cilt: 12 - Sayı: 2, 229–236. https://ajindex.com/articles/487511

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Cong, Y.; Yuan, J.; 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
  2. Wang, X.; Ma, X.; 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, Vol. 31, No. 3, 539–555.
  3. Wiliem, A.; Madasu, V.; Boles, W.; Yarlagadda, P. (2008).
  4. Detecting uncommon trajectories, Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, No. January, 398–404
  5. Sezer, E. S.; 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.
  6. Popoola, O. P.; Wang, K. (2012). Video-based abnormal human behavior recognitiona review, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 42, No. 6, 865–878.
  7. 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.
  8. Hu, M.; Ali, S.; Shah, M. (2008). Learning motion patterns in crowded scenes using motion flow field, Proceedings - International Conference on Pattern Recognition, 2–6.
  9. Chong, Y. S.; 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.
  10. Mehran, R.; Oyama, A.; Shah, M. (2009). Abnormal crowd behavior detection using social force model, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, Vol. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, No. 2, 935–942.
  11. Xu, J.; Denman, S.; Sridharan, S.; Fookes, C.; Rana, R. (2011). Dynamic Texture reconstruction from sparse codes for unusual event detection in crowded scenes, MM’11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops - JMRE 2011
  12. Workshop, J-MRE’11, 25–30.
  13. UCSD Anomaly Detection Dataset, URL: svcl.ucsd.edu/projects/anomaly/dataset.htm (Accessing Time; Mar