CFP last date
20 January 2025
Reseach Article

Exploring Behavior Analysis in Video Surveillance Applications

by Ahmed Taha, Hala H. Zayed, M. E. Khalifa, El-sayed M. El-horbaty
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 14
Year of Publication: 2014
Authors: Ahmed Taha, Hala H. Zayed, M. E. Khalifa, El-sayed M. El-horbaty
10.5120/16283-6045

Ahmed Taha, Hala H. Zayed, M. E. Khalifa, El-sayed M. El-horbaty . Exploring Behavior Analysis in Video Surveillance Applications. International Journal of Computer Applications. 93, 14 ( May 2014), 22-32. DOI=10.5120/16283-6045

@article{ 10.5120/16283-6045,
author = { Ahmed Taha, Hala H. Zayed, M. E. Khalifa, El-sayed M. El-horbaty },
title = { Exploring Behavior Analysis in Video Surveillance Applications },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 14 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 22-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number14/16283-6045/ },
doi = { 10.5120/16283-6045 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:44.210607+05:30
%A Ahmed Taha
%A Hala H. Zayed
%A M. E. Khalifa
%A El-sayed M. El-horbaty
%T Exploring Behavior Analysis in Video Surveillance Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 14
%P 22-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Video surveillance is recently one of the most active research topics in computer vision. It has a wide spectrum of promising public safety and security applications. As the number of cameras exceeds the capability of human operators to monitor them, the traditional passive video surveillance is proving ineffective. Hence, converting to intelligent visual surveillance is inevitable. Intelligent visual surveillance aims to detect, recognize and track certain objects from image sequences automatically, and more generally to understand and describe object behaviors. Many researchers have contributed to the field of automated video surveillance through detection, classification, and tracking algorithms. Despite recent progress in computer vision and other related areas, there are still major technical challenges to be overcome before reliable automated video surveillance can be realized. Recently, the problem of analyzing behavior in videos has been the focus of several researchers' efforts. It aims to analyze and interpret individual behaviors and interactions between different objects found in the scene. Hence, obtaining a description of what is happening in a monitored area, and then taking appropriate action based on that interpretation. In this paper, we give a survey of behavior analysis work in video surveillance and compare the performance of the state-of-the-art algorithms on different datasets. Moreover, useful datasets are analyzed in order to provide help for initiating research projects.

References
  1. NemanjaSpasic, "Anomaly Detection and Prediction of Human Actions in a Video Surveillance Environment," Master Thesis, Computer Science Dept. , Cape Town University, South Africa, December 2007.
  2. MahfuzulHaque, and ManzurMurshed, "Robust Background Subtraction Based on Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed," In Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops (ICMEW '12), Melbourne, Australia, pp. 396-401, July 2012.
  3. H. Wang, and Paul Miller, "Regularized Online Mixture of Gaussians for Background Subtraction," In Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS '11), Klagenfurt, Austria, pp. 249-254, September 2011.
  4. Vikas Reddy, Conrad Sanderson, Andres Sanin, and Brian C. Lovell, "MRF-Based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos," In Proceedings of the 10th Asian Conference on Computer Vision (ACCV'10), Queenstown, New Zealand, Volume Part III, pp. 547-559, November 2010.
  5. PawelForczmanski, and MarcinSeweryn, "Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition," In Proceedings of the 2010 International Conference on Computer Vision and Graphics (ICCVG'10), Warsaw, Poland, Part I, pp. 114–121, September 2010.
  6. Chris Poppe, Gaetan Martens, Peter Lambert, and Rik Van de Walle, "Improved Background Mixture Models for Video Surveillance Applications," In Proceedings of the 8th Asian Conference on Computer Vision (ACCV'07), Volume Part I, pp. 251–260, November 2007.
  7. Liman Liu, Wenbing Tao, Jin Liu, and JinwenTian, "A Variational Model and Graph Cuts Optimization For Interactive Foreground Extraction," In the Signal Processing Journal, Volume 91, Issue 5, May 2011.
  8. Xavier Suau, Josep R. Casas, and Javier Ruiz-Hidalgo, "Multi-Resolution Illumination Compensation for Foreground Extraction," In Proceedings of the 16th IEEE International Conference on Image Processing (ICIP'09), pp. 3189-3192, November 2009.
  9. Chen Change Loy, "Activity Understanding and Unusual Event Detection in Surveillance Videos," PhD dissertation, Queen Mary University of London, 2010.
  10. YanivGurwicz, RaananYehezkel, Boaz Lachover, "Multiclass Object Classification for Real-time Video Surveillance Systems," In Pattern Recognition Letters Journal, Volume 32, issue 6, pp. 805–815, April 2011.
  11. Bahad?r KARASULU, "Review and Evaluation of Well-Known Methods for Moving Object Detection and Tracking in Videos," In the Journal of Aeronautics and Space Technologies, Volume 4, Number 4, pp. 11-22, July 2010.
  12. Wenming Yang, Fei Zhou1, and Qingmin Liao, "Object Tracking and Local Appearance Capturing in a Remote Scene Video Surveillance System with Two Cameras," In Proceedings of the 16th International Multimedia Modeling Conference (MMM 2010), Chongqing, China, pp. 489–499, January 2010.
  13. Daniel Weinland, RemiRonfard, and Edmond Boyer, "A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition," In the Journal of Computer Vision and Image Understanding, Volume 115, Issue 2, pp. 224-241, February, 2011.
  14. Frederick Tung, John S. Zelek, and David A. Clausi, "Goal-Based Trajectory Analysis for Unusual Behaviour Detection in Intelligent Surveillance," In the Journal of Image and Vision Computing, Volume 29, Issue 4, March 2011.
  15. A. F. Bobick, and J. W. Davis, "The Recognition of Human Movement Using Temporal Templates," In IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 23, Number 3, pp. 257–267, 2001.
  16. S. Gong, and T. Xiang, "Scene Event Recognition Without Tracking," In ActaAutomaticaSinica Journal, Volume 29, Number 3, pp. 321–331, 2003.
  17. E. L. Andrade, S. Blunsden, and R. B. Fisher, "Hidden Markov Models for Optical Flow Analysis in Crowds," In Proceedings of the 18th International Conference on Pattern Recognition (ICPR '06), pp. 460-463, 2006.
  18. N. Dalal, and B. Triggs, "Histograms of Oriented Gradients for Human Detection," In Proceedings of the International IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893, June 2005.
  19. L. Zelnik-Manor, and M. Irani, "Statistical Analysis of Dynamic Actions," In IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 28, Number 9, pp. 1530–1535, 2006.
  20. L. Kratz and, K. Nishino, "Anomaly Detection in Extremely Crowded Scenes Using Spatiotemporal Motion Pattern Models," In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1446–1453, 2009.
  21. V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos, "Anomaly Detection in Crowded Scenes," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1975 - 1981, June 2010.
  22. J. Kim, and K. Grauman, "Observe Locally, Infer Globally: A Space-Time MRF for Detecting Abnormal Activities with Incremental Updates," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2928, 2009.
  23. T. Xiang, and S. Gong, "Video Behaviour Profiling for Anomaly Detection," In IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 30, Number 5, pp. 893–908, 2008.
  24. S. Park, and M. M. Trivedi, "A Two-Stage Multi-View Analysis Framework for Human Activity and Interactions," In Proceeding of IEEE Workshop on Motion and Video Computing, University of California, San Diego, pp. 29-34, February 2007.
  25. Rodrigo Cilla, Miguel A. Patricio, Antonio Berlanga, and José M. Molina, "Human Action Recognition with Sparse Classification and Multiple-View Learning," In Expert Systems Journal, Wiley Publishing Ltd, August 2013.
  26. Ronald Poppe, "A Survey on Vision-Based Human Action Recognition," In Image and Vision Computing Journal, Volume 28, Issue 6, pp. 976–990, June 2010.
  27. V. Parameswaran, and R. Chellappa, "View Invariance For Human Action Recognition," In the International Journal of Computer Vision, Volume 66, Issue 1, pp. 83-101, January 2006.
  28. V. Ferrari, M. Marin-Jimenez, and A. Zisserman, "Progressive Search Space Reduction for Human Pose Estimation," In Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), June 2008.
  29. Ziming Zhang, Yiqun Hu, Syin Chan and Liang-Tien Chia, "Motion Context: A New Representation for Human Action Recognition" In the European Conference on Computer Vision (ECCV), Marseille, France, October 2008.
  30. M. Rodriguez, J. Ahmed, and M. Shah, "Action MACH a Spatio-Temporal Maximum Average Correlation Height Filter for Action Recognition," In the IEEE Conference on Computer Vision and Pattern Recognition Action (CVPR 2008), pp. 1 - 8, June 2008.
  31. Shu-Fai Wong, "Extracting Spatiotemporal Interest Points Using Global Information," In Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV 2007), pp. 1-8, October 2007.
  32. Alireza Fathi, and Greg Mori, "Action Recognition by Learning Mid-Level Motion Features," In Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, Alaska, USA, pp. 1-8, June 2008.
  33. Chia-Chih Chen, J. K. Aggarwal, "Recognizing Human Action From a Far Field of View," In Proceeding of the 2009 International Conference on Motion and Video Computing (WMVC'09), Snowbird, Utah, pp. 119-125, December 2009.
  34. Andrew Gilbert, John Illingworth, and Richard Bowden, "Fast Realistic Multi-Action Recognition using Mined Dense Spatio-temporal Features," In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), Kyoto, Japan, pp. 925-931, October 2009.
  35. Dong Han, Liefeng Bo, and Cristian Sminchisescu, "Selection and Context for Action Recognition," In Proceedings of the IEEE International Conference on Computer Vision (ICCV), University of Bonn, Bonn, Germany, pp. 1933 - 1940, September 2009.
  36. Heng Wang, Muhammad MuneebUllah, Alexander Kläser, Ivan Laptev, CordeliaSchmid, "Evaluation of Local Spatio-Temporal Features for Action Recognition," In Proceedings of the British Machine Vision Conference (BMVC 2009), London, UK, pp. 124-134, September 2009.
  37. Kai Guo, PrakashIshwar, and JanuszKonrad, "Action Recognition Using Sparse Representation on Covariance Manifolds of Optical Flow," In Proceedings of the Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2010), pp. 188 - 195, September 2010.
  38. Saad Ali, and Mubarak Shah, "Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning," In the IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 32 , Issue 2, pp. 288 - 303, February 2010.
  39. Adriana Kovashka, and Kristen Grauman, "Learning a Hierarchy of Discriminative Space-Time Neighborhood Features for Human Action Recognition," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco, California, USA, pp. 2046-2053, June 2010.
  40. Yui Man Lui, J. Ross Beveridge, and Michael Kirby, "Action Classification on Product Manifolds," In Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco, CA, USA, pp. 833-839, June 2010.
  41. Matteo Bregonzio, Jian Li, Shaogang Gong, and Tao Xiang, "Discriminative Topics Modeling for Action Feature Selection and Recognition", In Proceedings of the British Machine Vision Conference (BMVC 2010), Aberystwyth, UK, pp. 1-11, September 2010.
  42. Hae Jong Seo, "Action Recognition from One Example," In IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 33, Issue 5, pp. 867 - 882, May 2011.
  43. Olusegun Oshin, Andrew Gilbert, and Richard Bowden "Capturing the Relative Distribution of Features for Action Recognition," In Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition (FG 2011), Santa Barbara, California, USA, pp. 111-116, March 2011.
  44. Yui Man Lui, and J. Ross Beveridge, "Tangent Bundle for Human Action Recognition," In Proceedings of the Ninth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), Santa Barbara, California, USA, pp. 97-102, March 2011.
  45. Sebastian Nowozin, Jamie Shotton, "Action Points: A Representation for Low-latency Online Human Action Recognition", Technical Report, Microsoft Research Cambridge, July 2012
  46. G Nagendar, Sai Ganesh, Mahesh Goud and C. V Jawahar "Action Recognition using Canonical Correlation Kernels," In Proceedings of the 11th Asian Conference on Computer Vision (ACCV 2012), Daejeon, Korea, November 2012.
  47. Esra Acar, Tobias Senst, Alexander Kuhn, Ivo Keller, Holger Theisel, Sahin Albayrak, and Thomas Sikora, "Human Action Recognition using Lagrangian Descriptors," In the 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), Banff, Canada, pp. 360-365, September 2012.
  48. Samy Sadek, Ayoub Al-Hamadi, Gerald Krell, and Bernd Michaelis, "Affine-Invariant Feature Extraction for Activity Recognitio," ISRN Machine Vision Journal, volume 2013, Article ID 215195, 7 pages, July 2013.
  49. J. Hernández, R. Cabido, A. S. Montemayor and J. J. Pantrigo "Human Activity Recognition Based on Kinematic Features", Expert Systems Journal, doi: 10. 1111/exsy. 12013, Volume 2013, February 2013.
  50. Tanfeng Sun, Xinghao Jiang, Chengming Jiang, Yaqing Li, "A Video Content Classification Algorithm Applying to Human Action Recognition," In the journal of Electronics and Electrical Engineering (Elektronika ir Elektrotechnika), ISSN 1392-1215, DOI: http://dx. doi. org/10. 5755/j01. eee. 19. 4. 4056, Volume 19, Number 4, 2013
  51. Michalis Vrigkas, Vasileios Karavasilis, Christophoros Nikou and Ioannis Kakadiaris, "Action Recognition by Matching Clustered Trajectories of Motion Vectors," In Proceedings of the 8th International Conference on Computer Vision Theory and Applications, Barcelona, Spain, February 2013.
  52. Georgios Goudelis, Konstantinos Karpouzis, and Stefanos Kollias, "Exploring Trace Transform for Robust Human Action Recognition," In Pattern Recognition Journal, Volume 46, Issue 12, pp. 3238–3248, December 2013.
  53. Li Liu, Ling Shao, and Peter Rockett, "Boosted Key-Frame Selection and Correlated Pyramidal Motion-Feature Representation for Human Action Recognition," In Pattern Recognition Journal, Volume 46, Issue 7, pp. 1810–1818, July 2013
  54. Li Wang, Ting Yun, and Haifeng Lin, "Boost Action Recognition through Computed Volume," In the Electrical Engineering Journal (TELKOMNIKA), Volume 11, Number 4, pp. 1871-1876, April 2013.
  55. Konstantinos G. Derpanis, Mikhail Sizintsev, Kevin J. Cannons, and Richard P. Wildes, "Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis," In the IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 35, Issue 3, pp. 527 - 540, March 2013
Index Terms

Computer Science
Information Sciences

Keywords

Behavior Analysis Action Recognition Event Detection.