Interest Area Localization using Trajectory Analysis in Surveillance Scenes

D. P. Dogra, A. Ahmed, H. Bhaskar

Abstract

In this paper, a method for detecting and localizing interest areas in a surveillance scene by analyzing the motion trajectories of multiple interacting targets, is proposed. Our method is based on a theoretical model representing the importance distribution of different areas (represented as a rectangular blocks) present in a surveillance scene. The importance of each block is modeled as a function of the total time spent by multiple targets and their relative velocity whilst passing through the blocks. Extensive experimentation and statistical validation with empirical data has shown that the proposed method follows the process of the theoretical model. The accuracy of our method in localizing interest areas has been verified and its superiority demonstrated against baseline methods using the publicly available: CAVIAR, ViSOR datasets and a scenario-specific in-house surveillance dataset.

References

  1. Beyan, C. and Fisher, R. (2013). Detection of abnormal fish trajectories using a clustering based hierarchical classifier. In British Machine Vision Conference, pages 21.1-21.11.
  2. Brun, L., S. A. and Vento, M. (2014). Dynamic scene understanding for behavior analysis based on string kernels. In IEEE Transactions on Circuits and Systems for Video Technology.
  3. Dinh, T., V. N. and Medioni, G. (2011). Context tracker: Exploring supporters and distracters in unconstrained environments. In Computer Vision and Pattern Recognition (CVPR), pages 1177-1184.
  4. Farabet, C., C. C. N. L. and LeCun, Y. (2013). Learning hierarchical features for scene labeling. In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1915-1929.
  5. Jiang, H., W. J. Y. Z. W. Y. Z. N. and Li, S. (2013). Salient object detection: A discriminative regional feature integration approach. In Computer Vision and Pattern Recognition (CVPR), pages 2083-2090.
  6. Margolin, R., T. A. and Zelnik-Manor, L. (2013). What makes a patch distinct? In Computer Vision and Pattern Recognition (CVPR). 1139-1146.
  7. Morris, B. and Trivedi, M. (2008). Learning and classification of trajectories in dynamic scenes: A general framework for live video analysis. In International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 154-161.
  8. Pan, J., F. Q. and Pankanti, S. (2011). Robust abandoned object detection using region-level analysis. In International Conference on Image Processing (ICIP), pages 3597-3600.
  9. Piciarelli, C. and Foresti, G. (2006). On-line trajectory clustering for anomalous events detection. Pattern Recognition Letters, 27(15):1835 - 1842. Vision for Crime Detection and Prevention.
  10. Piciarelli, C., M. C. and Foresti, G. (2008). Trajectorybased anomalous event detection. Circuits and Systems for Video Technology, IEEE Transactions on, 18(11):1544-1554.
  11. Rahtu, E., K. J. S. M. and Heikkila, J. (2010). Segmenting salient objects from images and videos. In European Conference on Computer Vision (ECCV), pages 366- 379.
  12. Saleemi, I., S. K. and Shah, M. (2009). Probabilistic modeling of scene dynamics for applications in visual surveillance. In IEEE Transactions on Pattern Analysis and Machine Intelligence, number 31(8), pages 1472-1485.
  13. Sharma, P. and Nevatia, R. (2013). Efficient detector adaptation for object detection in a video. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 3254-3261.
  14. Suzuki, N., H. K. T. K. K. Y. S. Y. and Fujino, Y. (2007). Learning motion patterns and anomaly detection by human trajectory analysis. In International Conference on Systems, Man and Cybernetics, pages 498- 503.
  15. Vezzani, R. and Cucchiara, R. (2010). Video surveillance online repository (visor): an integrated framework. In Multimedia Tools and Applications, number 50(2), pages 359-380.
  16. Wang, X., T. K. and Grimson, E. (2006). Learning semantic scene models by trajectory analysis. In European Conference on Computer Vision (ECCV), pages 110- 123.
  17. Xu, D., W. X. S. D. L. N. and Chen, Y. (2013). Hierarchical activity discovery within spatio-temporal context for video anomaly detection. In International Conference on Image Processing (ICIP), pages 3597-3601.
  18. Yang, Y., L. J. and Shah, M. (2009). Video scene understanding using multi-scale analysis. In Computer Vision and Pattern Recognition (CVPR), pages 1669- 1676.
  19. Zhou, Y., Y. S. and Huang, T. (2007). Detecting anomaly in videos from trajectory similarity analysis. In International Conference on Multimedia and Expo (ICME), pages 1087-1090.
Download


Paper Citation


in Harvard Style

Dogra D., Ahmed A. and Bhaskar H. (2015). Interest Area Localization using Trajectory Analysis in Surveillance Scenes . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 478-485. DOI: 10.5220/0005334704780485


in Bibtex Style

@conference{visapp15,
author={D. P. Dogra and A. Ahmed and H. Bhaskar},
title={Interest Area Localization using Trajectory Analysis in Surveillance Scenes},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={478-485},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005334704780485},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Interest Area Localization using Trajectory Analysis in Surveillance Scenes
SN - 978-989-758-090-1
AU - Dogra D.
AU - Ahmed A.
AU - Bhaskar H.
PY - 2015
SP - 478
EP - 485
DO - 10.5220/0005334704780485