BibTex RIS Cite
Year 2015, Volume: 36 Issue: 3, 3059 - 3065, 13.05.2015

Abstract

References

  • S. Ali and M. Shah. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In CVPR, 2007.
  • Basharat, Y. Zhai, and M. Shah. Content based video matching using spatiotemporal volumes. Comput. Vision Image Understanding, 110(3):360–377, 2008.
  • H. Buxton. Generative Models for Learning and Understanding Dynamic Scene Activity. Workshop on GMBV, 2002.
  • M. Figueiredo and A. Jain. Unsupervised learning of finite mixture models. PAMI, IEEE Transactions on, 2002.
  • W. Grimson, C. Stauffer, R. Romano, and L. Lee. Using adaptive tracking to classify and monitor activities in asite. CVPR, 1998.
  • O. Javed and M. Shah. Tracking and object classification for automated surveillance. ECCV, 2002.
  • N. Johnson and D. Hogg. Learning the distribution of object trajectories for event recognition. BMVC, 1995.J. Kim and K. Grauman. Observe locally, infer globally: a space- time mrf for detecting abnormal activities with incremental updates. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 2009.
  • L. Kratz and K. Nishino. Anomaly detection in extremely crowded scenes using spatiotemporal motion pattern models. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 2009.
  • R. Mehran, A. Oyama, and M. Shah. Abnormal crowd behavior detection using social force model. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 0:935–942, 2009.
  • P. Remagnino and G. Jones. Classifying Surveillance Events from Attributes and Behaviour. BMVC, 2001.
  • Saleemi, K. Shafique, and M. Shah. Probabilistic modeling of scene dynamics for applications in visual surveillance. Accepted for Publication in TPAMI, 2008.

A Novel Method for Scene Modeling to Detect Unusual Activity

Year 2015, Volume: 36 Issue: 3, 3059 - 3065, 13.05.2015

Abstract

Abstract. Automated video surveillance is crucial for the security of various sites including airports, train stations, military bases, and many other public facilities. A modern surveillance system is expected to not only perform basic object detection and tracking, but also to interpret object behaviors. This higher level interpretation can have several applications including abnormal behavior detection, analysis of traffic trends, and improving object detection and tracking. In this paper we focus on the problem of interpreting the output of the object detection and tracking module in order to gather knowledge about the scene. This knowledge is used to build a scene model which can be used to detect abnormal motion patterns and to enhance the surveillance performance by improving object detection. We present two novel and complementing models here:  first model that is suitable for modeling single object motion, and real-time applications and second model that is useful for learning relationship between concurrently moving object pairs in the scene.

References

  • S. Ali and M. Shah. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In CVPR, 2007.
  • Basharat, Y. Zhai, and M. Shah. Content based video matching using spatiotemporal volumes. Comput. Vision Image Understanding, 110(3):360–377, 2008.
  • H. Buxton. Generative Models for Learning and Understanding Dynamic Scene Activity. Workshop on GMBV, 2002.
  • M. Figueiredo and A. Jain. Unsupervised learning of finite mixture models. PAMI, IEEE Transactions on, 2002.
  • W. Grimson, C. Stauffer, R. Romano, and L. Lee. Using adaptive tracking to classify and monitor activities in asite. CVPR, 1998.
  • O. Javed and M. Shah. Tracking and object classification for automated surveillance. ECCV, 2002.
  • N. Johnson and D. Hogg. Learning the distribution of object trajectories for event recognition. BMVC, 1995.J. Kim and K. Grauman. Observe locally, infer globally: a space- time mrf for detecting abnormal activities with incremental updates. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 2009.
  • L. Kratz and K. Nishino. Anomaly detection in extremely crowded scenes using spatiotemporal motion pattern models. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 2009.
  • R. Mehran, A. Oyama, and M. Shah. Abnormal crowd behavior detection using social force model. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 0:935–942, 2009.
  • P. Remagnino and G. Jones. Classifying Surveillance Events from Attributes and Behaviour. BMVC, 2001.
  • Saleemi, K. Shafique, and M. Shah. Probabilistic modeling of scene dynamics for applications in visual surveillance. Accepted for Publication in TPAMI, 2008.
There are 11 citations in total.

Details

Journal Section Special
Authors

Javad Haddadnıa

Hamidreza Rabıee This is me

Omid Rahmanı Seryasat This is me

Publication Date May 13, 2015
Published in Issue Year 2015 Volume: 36 Issue: 3

Cite

APA Haddadnıa, J., Rabıee, H., & Rahmanı Seryasat, O. (2015). A Novel Method for Scene Modeling to Detect Unusual Activity. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 3059-3065.
AMA Haddadnıa J, Rabıee H, Rahmanı Seryasat O. A Novel Method for Scene Modeling to Detect Unusual Activity. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. May 2015;36(3):3059-3065.
Chicago Haddadnıa, Javad, Hamidreza Rabıee, and Omid Rahmanı Seryasat. “A Novel Method for Scene Modeling to Detect Unusual Activity”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36, no. 3 (May 2015): 3059-65.
EndNote Haddadnıa J, Rabıee H, Rahmanı Seryasat O (May 1, 2015) A Novel Method for Scene Modeling to Detect Unusual Activity. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36 3 3059–3065.
IEEE J. Haddadnıa, H. Rabıee, and O. Rahmanı Seryasat, “A Novel Method for Scene Modeling to Detect Unusual Activity”, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, vol. 36, no. 3, pp. 3059–3065, 2015.
ISNAD Haddadnıa, Javad et al. “A Novel Method for Scene Modeling to Detect Unusual Activity”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36/3 (May 2015), 3059-3065.
JAMA Haddadnıa J, Rabıee H, Rahmanı Seryasat O. A Novel Method for Scene Modeling to Detect Unusual Activity. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36:3059–3065.
MLA Haddadnıa, Javad et al. “A Novel Method for Scene Modeling to Detect Unusual Activity”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, vol. 36, no. 3, 2015, pp. 3059-65.
Vancouver Haddadnıa J, Rabıee H, Rahmanı Seryasat O. A Novel Method for Scene Modeling to Detect Unusual Activity. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36(3):3059-65.