A Prototype Application for Long-time Behavior Modeling and Abnormal Events Detection

Nicoletta Noceti, Francesca Odone

2016

Abstract

In this work we present a prototype application for modelling common behaviours from long-time observations of a scene. The core of the system is based on the method proposed in (Noceti and Odone, 2012), an adaptive technique for profiling patterns of activities on temporal data -- coupling a string-based representation and an unsupervised learning strategy -- and detecting anomalies --- i.e., dynamic events diverging with respect to the usual dynamics. We propose an engineered framework where the method is adopted to perform an online analysis over very long time intervals (weeks of activity). The behaviour models are updated to accommodate new patterns and cope with the physiological scene variations. We provide a thorough experimental assessment, to show the robustness of the application in capturing the evolution of the scene dynamics.

References

  1. Brox, T. and Malik, J. (2010). Object segmentation by long term analysis of point trajectories. In ECCV, pages 282-295.
  2. Bulpitt, A. and Sumpter, N. (2000). Learning spatiotemporal patterns for predicting object behaviour. IMAVIS, 18(9):697-704.
  3. Chen, C. and Aggarwal, J. (2011). Modeling human activities as speech. In CVPR, pages 3425-3432.
  4. Cheng, K.-W., Chen, Y.-T., and Fang, W.-H. (2015). Video anomaly detection and localization using hierarchical feature representation and gaussian process regression. In CVPR, pages 2909-2917.
  5. F. Bashir, A. K. and Schonfeld., D. (2007). Object trajectory-based activity classification and recognition using hidden markov model. IP, 16(7):1912-1919.
  6. Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., and Maybank, S. (2006). A system for learning statistical motion patterns. PAMI, 28(9):1450-1464.
  7. Hu, W., Xie, D., Fu, Z., Zeng, W., and Maybank, S. (2007). Semantic-based surveillance video retrieval. IP, 16(4):1168-1181.
  8. Javed, I. J. O. and Shah, M. (2004). Multi feature path modeling for video-surveillance. In ICPR, pages 716-719.
  9. Johnson, N. and Hogg, D. (1995). Learning the distribution of object trajectories for event recognition. In BMVC, volume 2, pages 583-592.
  10. Morris, B. and Trivedi, M. (2008). A survey of vision-based trajectory learning and analysis for surveillance. Circ. and Sys. for Video Tech., 18(8):1114-1127.
  11. Morris, B. and Trivedi, M. M. (2009). Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In CVPR, pages 312-319.
  12. Noceti, N., Destrero, A., Lovato, A., and Odone, F. (2009). Combined motion and appearance models for robust object tracking in real-time. In AVSS, pages 412-419.
  13. Noceti, N. and Odone, F. (2012). Learning common behaviors from large sets of unlabeled temporal series. Image and Vision Computing, 30(11):875-895.
  14. Noceti, N., Santoro, M., and Odone, F. (2011). Learning behavioral patterns of time series for videosurveillance. In Learning Behavioral Patterns of Time Series for Video-Surveillance (Springer), pages 275- 304. Springer-London.
  15. Ren, H., Liu, W., Olsen, S., and Moeslund, T. (2015). Unsupervised behaviour-specific dictionary learning in abnormal event detection. In BMVC.
  16. Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. Trans. on PAMI, 22(8):888-905.
  17. Stauffer, C. and Grimson, E. (2000). Learning patterns of activity using real-time tracking. PAMI, 22(8):747- 757.
  18. Taylor, J. S. and Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
  19. Xu, D., Ricci, E., Yan, Y., Song, J., and Sebe, N. (2015). Learning deep representations of appearance and motion for anomalous event detection. In BMVC.
Download


Paper Citation


in Harvard Style

Noceti N. and Odone F. (2016). A Prototype Application for Long-time Behavior Modeling and Abnormal Events Detection . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 597-604. DOI: 10.5220/0005723105970604


in Bibtex Style

@conference{visapp16,
author={Nicoletta Noceti and Francesca Odone},
title={A Prototype Application for Long-time Behavior Modeling and Abnormal Events Detection},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={597-604},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005723105970604},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - A Prototype Application for Long-time Behavior Modeling and Abnormal Events Detection
SN - 978-989-758-175-5
AU - Noceti N.
AU - Odone F.
PY - 2016
SP - 597
EP - 604
DO - 10.5220/0005723105970604