IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Anomalous Trajectory Detection in Surveillance Systems Using Pedestrian and Surrounding Information

  • Doan, Trung Nghia (Inter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University) ;
  • Kim, Sunwoong (Inter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University) ;
  • Vo, Le Cuong (School of Electronics and Telecommunications, Hanoi University of Science and Technology) ;
  • Lee, Hyuk-Jae (Inter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University)
  • Received : 2016.08.01
  • Accepted : 2016.08.28
  • Published : 2016.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

Keywords

References

  1. B. T. Morris and M. M. Trivedi, "A survey of visionbased trajectory learning and analysis for surveillance," Circuits and Systems for Video Technology, IEEE Transactions on 18.8 (2008): 1114-1127. https://doi.org/10.1109/TCSVT.2008.927109
  2. P. Remagnino, S. A. Velastin, G. L. Foresti, and M. Trivedi, "Novel concepts and challenges for the next generation of video surveillance systems," Machine Vision and Applications 18.3 (2007): 135-137. https://doi.org/10.1007/s00138-006-0059-6
  3. M. Shah, "Understanding human behaviour from motion imagery," Machine Vision and Applications 14.4 (2003): 210-214. https://doi.org/10.1007/s00138-003-0124-3
  4. W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, and S. Maybank, "A system for learning statistical motion patterns," Pattern Analysis and Machine Intelligence, IEEE Transactions on 28.9 (2006): 1450-1464. https://doi.org/10.1109/TPAMI.2006.176
  5. C. Jung, L. Hennemann, and S. R. Musse, "Event detection using trajectory clustering and 4-D histograms," Circuits and Systems for Video Technology, IEEE Transactions on 18.11 (2008): 1565-1575. https://doi.org/10.1109/TCSVT.2008.2005600
  6. R. Laxhammar and G. Falkman, "Online learning and sequential anomaly detection in trajectories," Pattern Analysis and Machine Intelligence, IEEE Transactions on 36.6 (2014): 1158-1173. https://doi.org/10.1109/TPAMI.2013.172
  7. H. Jeong, H. J. Chang, and J. Y. Choi, "Modeling of moving object trajectory by spatio-temporal learning for abnormal behaviour detection," Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on. IEEE, 2011.
  8. G. Fuhr and C. R. Jung, "Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras," Pattern Recognition Letters 39 (2014): 11-20. https://doi.org/10.1016/j.patrec.2013.08.031
  9. A. Milan, L. Leal-Taixe, K. Schindler, and I. Reid, "Joint tracking and segmentation of multiple targets," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
  10. P. Dollar, R. Appel, S. Belongie, and P. Perona, "Fast feature pyramids for object detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on 36.8 (2014): 1532-1545. https://doi.org/10.1109/TPAMI.2014.2300479
  11. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models," Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.9 (2010): 1627-1645. https://doi.org/10.1109/TPAMI.2009.167
  12. X. Ren and J. Malik, "Learning a classification model for segmentation," Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on. IEEE, 2003.
  13. G. Mori, X. Ren, A. A. Efros, and J. Malik, "Recovering human body configurations: Combining segmentation and recognition," Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on. Vol. 2. IEEE, 2004.
  14. J. Ferryman and A. Ellis, "PETS2010: Dataset and challenge," Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on. IEEE, 2010.
  15. B. Benfold and I. Reid, "Stable multi-target tracking in real-time surveillance video," Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.