Abnormal behavior detection based on dense trajectories
Shatalin R.A., Fidelman V.R., Ovchinnikov P.E.

 

Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia

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Abstract:
In this paper, we propose abnormal behavior detection algorithms based on dense trajectories and principal components for video surveillance applications. The result shows that the proposed algorithms are faster than an algorithm based on lengths of displacement vectors but the accuracy is only retained if the bag-of-features model is trained on a balanced sample of behavior features.

Keywords:
video surveillance, abnormal behaviour detection, principal component analysis, dense trajectories.

Citation:
Shatalin RA, Fidelman VR, Ovchinnikov PE. Abnormal behavior detection based on dense trajectories. Computer Optics 2018; 42(3): 476-482. DOI: 10.18287/2412-6179-2018-42-3-476-482.

References:

  1. Sodemann A, Ross M, Borghetti B. A review of anomaly detection in automated surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 2012, 42(6): 1257-1272. DOI: 10.1109/TSMCC.2012.2215319.
  2. Epifancev BN, Pyatkov AA, Kopeykin SA. Multi-sensor systems for monitoring access to restricted areas: capabilities of the intrusion detection video analytical channel. Computer Optics 2016; 40(1): 121-129. DOI: 10.18287/2412-6179-2016-40-1-121-129.
  3. Wang H, Kläser A, Schmid C, Liu C. Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision, 2013, 103(1): 60-79. DOI: 10.1007/s11263-012-0594-8.
  4. Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM Computing Surveys 2009; 41(3): 15.  DOI: 10.1145/1541880.1541882.
  5. Jolliffe IT. Principal component analysis. 2nd ed. Springer, New York: Springer-Verlag; 2002. ISBN: 978-0-387-95442-4.
  6. Shatalin RA, Fidelman VR, Ovchinnikov PE. Abnormal behaviour detection method for video surveillance applications [In Russian]. Computer Optics 2017; 41(1): 37-45. DOI: 10.18287/2412-6179-2017-41-1-37-45.
  7. He H, Garcia EA. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 2009; 21(9): 1263-1284. DOI: 10.1109/TKDE.2008.239.
  8. Akimov AV, Sirota AA. Synthetic data generation models and algorithms for training image recognition algorithms using the Viola-Jones framework [In Russian]. Computer Optics 2016; 40(6): 911-918. DOI: 10.18287/2412-6179-2016-40-6-911-918.
  9. Maddalena L, Petrosino A. A self-organizing approach to background subtraction for visual surveillance application. IEEE Transactions on Image Processing 2008; 17(7): 1168-1177. DOI: 10.1109/TIP.2008.924285.
  10. Ovchinnikov PE, Shatalin RA. Background subtraction quality criterion based on morphological operations for behaviour anomaly detection [In Russian]. Systemy upravleniya i informacionnye technologii 2014; 56(2): 190-194.
  11. Bouguet JY. Pyramidal implementation of the lucas kanade feature tracker. Intel Corporation, Microprocessor Research Labs 2000.
  12. Antonakaki P, Kosmopoulos D, Perantonis S. Detecting abnormal human behavior using multiple cameras. Signal Proccesing 2009; 89(9): 1723-1738. DOI: 10.1016/j.sigpro.2009.03.016.
  13. Mahadevan V, Li W, Bhalodia V, Vasconcelos N. Anomaly detection and localization in crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(1): 18-32. DOI: 10.1109/TPAMI.2013.111.
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