Video Gözetim Sistemlerinde Anomali Tespiti Üzerine Bir Derleme

Yıl 2016, , 0 - 0, 30.09.2016

Kadriye Öz , Salih Görgünoğlu

https://doi.org/10.31202/ecjse.258578

Cited By: 2

Öz

Günümüzde yaygın olarak kullanılan kameralar otomatik gözetim sistemlerinin gelişmesine katkıda bulunmuştur. Gözetim sistemleri ile birlikte video görüntülerinde  olağandışı durumların tespiti çalışmalarına olan ilgi artmıştır. Anomali olarak da isimlendirilebilen bu durumlar başta güvenlik olmak üzere pek çok alanda kullanılmaktadır.  Bu çalışmada video görüntülerinde anomali tespiti ve ilgili çalışmalar incelenmiştir. Anomali tespiti ve gözetim, özellik çıkarımı, eğitim ve öğrenme, modelleme ve sınıflandırma algoritmaları üzerinde durulmuştur.

Anahtar Kelimeler

Video analizi, anomali tespiti, video gözetim sistemleri, anomali tespit yöntemleri

Kaynakça

• V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: A survey, ACM Comput. Surv. 41 (2009) 1–58.
• A. a. Sodemann, M.P. Ross, B.J. Borghetti, A review of anomaly detection in automated surveillance, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42 (2012) 1257–1272.
• O.P. Popoola, K. Wang, Video-Based Abnormal Human Behavior Recognition: A Review, Syst. Man, Cybern. Part C Appl. Rev. IEEE Trans. 42 (2012) 865–878.
• V.J. Hodge, J. Austin, A Survey of Outlier Detection Methodoligies, Artif. Intell. Rev. 22 (2004) 85–126.
• T. Li, H. Chang, M. Wang, B. Ni, R. Hong, Crowded Scene Analysis : A Survey, 25 (2015) 367–386.
• T. V Duong, H.H. Bui, D.Q. Phung, S. Venkatesh, Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model, in: Proc. 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2005.
• H. Foroughi, a. Rezvanian, a. Paziraee, Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine, 2008 Sixth Indian Conf. Comput. Vision, Graph. Image Process. (2008).
• Y. Wang, K. Huang, T. Tan, Abnormal Actıvıty Recognıtıon In Offıce Based On R Transform, IEEE Int. Conf. Image Process. (2007) 209–212.
• U. ER, Video Görüntülerinden Trafik Kazası Riskini Gerçek Zamanlı Belirleyen Bir Sistem Tasarımı, Yıldız Teknik Üniversitesi, 2012.
• X. Mo, V. Monga, R. Bala, Simultaneous sparsity model for multi-perspective video anomaly detection, ICIP. (2014) 2314–2318.
• Y.-K. Wang, C.-T. Fan, J.-F. Chen, Traffic Camera Anomaly Detection, 2014 22nd Int. Conf. Pattern Recognit. (2014) 4642–4647.
• M.M.L. Elahi, R. Yasir, M.A. Syrus, S.Q.Z. Nine, I. Hossain, N. Ahmed, Computer Vison Based Road Traffic Accident and Anomaly Detection in the Context of Bangladesh, in: 2014.
• W. Lin, Y. Zhang, J. Lu, B. Zhou, J. Wang, Y. Zhou, Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis, Neurocomputing. 155 (2015) 84–98.
• R. Mehran, A. Oyama, M. Shah, Abnormal crowd behavior detection using social force model, 2009 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. CVPR Work. 2009. (2009) 935–942.
• C. Ongun, A. Temizel, T. Taşkaya Temizel, Local Anomaly Detection in Crowded Scenes Using Finite - Time Lyapunov Exponent Based Clustering, 11th IEEE Int. Conf. Adv. Video Signal Based Surveill. (2014) 331–336.
• X. Zhu, J. Liu, J. Wang, C. Li, H. Lu, Sparse representation for robust abnormality detection in crowded scenes, Pattern Recognit. 47 (2014) 1791–1799.
• B. Yogameena, K.S. Priya, Synoptic Video Based Human Crowd Behavior Analysis for Forensic Video Surveillance, Adv. Pattern Recognit. (ICAPR), 2015 Eighth Int. Conf. (2015).
• D. Pathak, A. Sharang, A. Mukerjee, Anomaly Localization in Topic-Based Analysis of Surveillance Videos, 2015 IEEE Winter Conf. Appl. Comput. Vis. (2015) 389–395.
• Y. Zhu, N.M. Nayak, a K. Roy-Chowdhury, Context-Aware Activity Recognition and Anomaly Detection in Video, Sel. Top. Signal Process. IEEE J. 7 (2013) 91–101.
• M.J. Roshtkhari, M.D. Levine, Online dominant and anomalous behavior detection in videos, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (2013) 2611–2618.
• S. Kwak, H. Byun, Detection of dominant flow and abnormal events in surveillance video, Opt. Eng. 50 (2011) 027202.
• J. Snoek, J. Hoey, L. Stewart, R.S. Zemel, A. Mihailidis, Automated detection of unusual events on stairs, Image Vis. Comput. 27 (2009) 153–166.
• A.E. Gunduz, A. Temizel, T. Taskaya Temizel, Feature detection and tracking for extraction of crowd dynamics, 2013 21st Signal Process. Commun. Appl. Conf. (2013) 1–4.
• V. Reddy, C. Sanderson, B.C. Lovell, Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture, IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. (2011).
• S. Wu, B.E. Moore, M. Shah, Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (2010) 2054–2060.
• D. Duque, H. Santos, P. Cortez, Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems, 2007 IEEE Symp. Comput. Intell. Data Min. (2007) 362–367.
• C. Li, Z. Han, Q. Ye, J. Jiao, Visual abnormal behavior detection based on trajectory sparse reconstruction analysis, Neurocomputing. 119 (2013) 94–100.
• A. Wiliem, V. Madasu, W. Boles, P. Yarlagadda, Detecting uncommon trajectories, Proc. - Digit. Image Comput. Tech. Appl. DICTA 2008. (2008) 398–404.
• H. Li, A. Achim, D. Bull, Unsupervised video anomaly detection using feature clustering, IET Signal Process. 6 (2012) 521.
• F. Nater, H. Grabner, T. Jaeggli, L. Van Gool, Tracker trees for unusual event detection, 2009 IEEE 12th Int. Conf. Comput. Vis. Work. ICCV Work. 2009. (2009) 1113–1120.
• Y. Zhou, S. Yan, T.S. Huang, Detecting Anomaly In Videos From Trajectory Similarity Analysis, Multimed. Expo, 2007 IEEE Int. Conf. (2007) 2007–2010.
• D.H. Hu, X.X. Zhang, J. Yin, V.W. Zheng, Q. Yang, Abnormal activity recognition based on HDP-HMM models, IJCAI Int. Jt. Conf. Artif. Intell. (2009) 1715–1720.
• X. Wang, S. Member, X. Ma, S. Member, Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models, Pattern Anal. Mach. Intell. IEEE Trans. 31 (2009) 539–555.
• A. Vandecasteele, R. Devillers, A. Napoli, A semi-supervised learning framework based on spatio-temporal semantic events for maritime anomaly detection and behavior analysis, (2013) 9–12.
• R.R. Sillito, R.B. Fisher, Semi-supervised Learning for Anomalous Trajectory Detection, in: BMVC 2008, 2008: pp. 1035–1044.
• D. Xu, R. Song, X. Wu, N. Li, W. Feng, H. Qian, Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts, Neurocomputing. 143 (2014) 144–152.
• F. Jiang, Y. Wu, A.K. Katsaggelos, A dynamic hierarchical clustering method for trajectory-based unusual video event detection, IEEE Trans. Image Process. 18 (2009) 907–913.
• G. Zhou, Y. Wu, Anomalous Event Detection Based on Self-Organizing Map for Supermarket Monitoring, 2009 Int. Conf. Inf. Eng. Comput. Sci. (2009) 1–4.
• a. Mecocci, M. Pannozzo, a. Fumarola, Automatic detection of anomalous behavioural events for advanced real-time video surveillance, 3rd Int. Work. Sci. Use Submar. Cables Relat. Technol. 2003. (2003) 29–31.
• S. Biswas, R.V. Babu, Real time anomaly detection in H.264 compressed videos, 2013 Fourth Natl. Conf. Comput. Vision, Pattern Recognition, Image Process. Graph. (2013) 1–4.
• H. Wang, R. Fu, N. Li, G. Liang, X. Wu, Anomaly Detection in Crowds Assisted by Scene Perspective Projection Correction, IEEE. (2014) 14–17.
• B. Wenger, S. Mandayam, P.J. Violante, K.J. Drake, Detection of anomalous events in shipboard video using moving object segmentation and tracking, Autotestcon, 2010 Ieee. (2010) 1–6.
• X. Zou, B. Bhanu, Anomalous activity classification in the distributed camera network, Proc. - Int. Conf. Image Process. ICIP. (2008) 781–784.
• Z. Jun, L. Yushu, L. Xuhong, Anomalous detection based on adaboost-HMM, Proc. World Congr. Intell. Control Autom. 1 (2006) 4360–4363.
• Z. Jun, L. Zhijing, Detecting irregularities by image contour based on fuzzy neural network, 3rd Int. Conf. Innov. Comput. Inf. Control. ICICIC’08. (2008) 0–3.