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On Video Based Human Abnormal Activity Detection with Histogram of Oriented Gradients

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Handbook of Multimedia Information Security: Techniques and Applications

Abstract

Video based activity analysis has applications in surveillance and monitoring, and in recent years various automatic analysis techniques were used to obtain efficient detection of activities. As surveillance cameras are becoming ubiquitous, automatic human activity analysis and abnormal activity recognition in videos are required to handle the big streaming data from these sensors. However, automatic video examination remains challenging due to inter-object occlusions in big crowded scenes, unconstrained motions of people’s activities, and also the quality limitation of acquired videos. Further, to attain robust detection of abnormal actions from surveillance videos there needs to be a separate module of computations that can handle vague, infrequent occurring abnormal activities from noise. In this chapter, we propose a method, that can recognize abnormal activities based on the histogram of oriented gradients approach, that can handle the imprecise visual observations, as well as overcome irregularity of other activity recognition approaches. We develop a static camera human detection with local feature extraction and use Support Vector Machine (SVM) for classification of abnormal activity detection from videos. Experimental results indicate the promise of our approach in with good precision with less false positive activity frames.

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References

  1. M. W. Green, "The appropriate and effective use of security technologies in U.S. schools." Technical Report NCJ 178265, Sandia National Laboratories, 1999.

    Google Scholar 

  2. Shian-Ru Ke, Hoang Le Uyen Thuc, Yong-Jin Lee, Jenq-Neng Hwang, Jang-Hee Yoo, and Kyoung-Ho Choi, "A review on video-based human activity recognition." Computers, 2(2):88–131, 2013.

    Article  Google Scholar 

  3. Blank, M.; Gorelick, L.; Shechtman, E.; Irani, M.; Basri, R, "Actions as Space-time Shapes." In Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV), Beijing, China, 17–21 October 2005; Volume 2, pp. 1395–1402.

    Google Scholar 

  4. Ke, Y.; Sukthankar, R.; Hebert, M, "Spatio-temporal Shape and Flow Correlation for Action Recognition." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN, USA, 17–22 June 2007; pp. 1–8.

    Google Scholar 

  5. Kumari, S.; Mitra, S.K., "Human Action Recognition Using DFT." In Proceedings of the third IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Hubli, India, 15–17 December 2011; pp. 239–242.

    Google Scholar 

  6. Lowe, D.G, "Distinctive image features from scale-invariant keypoints." Int. J. Comput. Vis. 2004, 60, 91–110.

    Article  Google Scholar 

  7. Dalal, N,; Triggs, B, "Histograms of oriented Gradients for Human Detection". In proceedings of IEEE Computer Society Conference on Computer vision and Pattern Recognition (CVPR), San Diego, CA, USA, 20-26 June 2005; Volume 1, pp. 886-893

    Google Scholar 

  8. Lin, C.; Hsu, F.; Lin, W, "Recognizing human actions using NWFE-based histogram vectors." EURASIP J. Adv. Signal Process. 2010, 2010, 9.

    Google Scholar 

  9. Veeraraghavan, A.; Roy-Chowdhury, A.K.; Chellappa, R, "Matching shape sequences in video with applications in human movement analysis." IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27, 1896–1909.

    Google Scholar 

  10. Leong, I.; Fang, J.; Tsai, M, "Automatic body feature extraction from a marker-less scanned human body." Comput.-Aided Des. 2007, 39, 568–582.

    Article  Google Scholar 

  11. Lucas, B.D.; Kanade, T, "An Iterative Image Registration Technique with An Application to Stereo Vision." In Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, B.C., Canada, 24–28 August 1981.

    Google Scholar 

  12. Ramanan, D.; Forsyth, D.A, "Finding and Tracking People from the Bottom Up." In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, 16–22 June 2003; Volume 2, pp. II-467–II-474.

    Google Scholar 

  13. Prasath, V. B. S., Pelapur, R., Palaniappan, K., and Seetharaman, G. (2014). Feature fusion and label propagation for textured object video segmentation. In Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Vol. 9089, p. 908904). International Society for Optics and Photonics.

    Google Scholar 

  14. Hoang, L.U.T.; Ke, S.; Hwang, J.; Yoo, J.; Choi, K, "Human Action Recognition based on 3D Body Modeling from Monocular Videos." In Proceedings of Frontiers of Computer Vision Workshop, Tokyo, Japan, 2–4 February 2012; pp. 6–13.

    Google Scholar 

  15. Ramanan, D.; Forsyth, D.A.; Zisserman, A, "Strike a Pose: Tracking People by Finding Stylized Poses." In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, 20–26 June 2005; vol. 1, pp. 271–278

    Google Scholar 

  16. Hoang, L.U.T.; Ke, S.; Hwang, J; Tuan, P.V.; Chau, T.N, "Quasi-periodic Action Recognition from Monocular Videos via 3D Human Models and Cyclic HMMs." In Proceedings of IEEE International Conference on Advanced Technologies for Communications (ATC), Hanoi, Vietnam, 10–12 October 2012; pp. 110–113.

    Google Scholar 

  17. Sempena, S.; Maulidevi, N.U.; Aryan P.R, "Human Action Recognition Using Dynamic Time Warping." In IEEE International Conference on Electrical Engineering and Informatics (ICEEI), Bandung, Indonesia, 17–19 July 2011; pp. 1–5.

    Google Scholar 

  18. Rabiner, L.; Juang, B, "Fundamentals of Speech Recognition;" Prentice Hall: Englewood Cliffs, NJ, USA, 1993.

    Google Scholar 

  19. Foroughi, H.; Rezvanian, A.; Paziraee, A, "Robust Fall Detection Using Human Shape and Multi-Class Support Vector Machine." In Proceedings of the IEEE Sixth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP), Bhubaneswar, India, 16–19 December 2008; pp. 413–420

    Google Scholar 

  20. Jain, A.K.; Duin, R.P.W.; Mao, J, "Statistical pattern recognition: A review. IEEE Trans. Pattern Anal." Mach. Intell. 2000, 22, 4–37.

    Article  Google Scholar 

  21. Chu, C.; Hwang, J.; Wang, S.; Chen, Y, "Human Tracking by Adaptive Kalman Filtering and Multiple Kernels Tracking with Projected Gradients." In Proceedings of IEEE Fifth ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Ghent, Belgium, 23–26 August 2011; pp. 1–6.

    Google Scholar 

  22. Schuldt, C.; Laptev, I.; Caputo, B, "Recognizing Human Actions: A Local SVM Approach." In Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR), Cambridge, UK, 23–26 August 2004; Volume 3, pp. 32–36.

    Google Scholar 

  23. Tipping, M.E, "The relevance vector machine." Adv. Neural Inf. Process. Syst. 2000, 12, 652–658.

    Google Scholar 

  24. Tipping, M.E, "Sparse Bayesian learning and the relevance vector machine." J. Mach. Learn. Res. 2001, 1, 211–244.

    Google Scholar 

  25. Fiaz, M.K.; Ijaz, B, "Vision based Human Activity Tracking using Artificial Neural Networks." In Proceedings of IEEE International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, 15–17 June 2010; pp. 1–5.

    Google Scholar 

  26. Rabiner, L.; Juang, B, "An introduction to hidden Markov models." IEE ASSP Mag. 1986, 3, 4–16.

    Article  Google Scholar 

  27. J. Black, S. A. Velastin, and B. Boghossian, “A real time surveillance system for metropolitan railways,” in Proc. IEEE Conf. Adv. Video Signal based Surveillance, 2005, pp. 189–194.

    Google Scholar 

  28. D. Aubert, F. Guichard, and S. Bouchafa, “Time-scale change detection applied to real-time abnormal stationarity monitoring,” Real-Time Imaging, vol. 10, no. 1, pp. 9–22, Feb. 2004.

    Google Scholar 

  29. Töreyin, B.U.; Dedeoğlu, Y.; Çetin, A.E, "HMM based falling person detection using both audio and video." In Proceedings of the 2005 International Conference on Computer Vision (ICCV) in Human-Computer Interaction, Beijing, China, 17–20 October; pp. 211–220.

    Google Scholar 

  30. Shieh, W.; Huang, J. Speedup the Multi-Camera, "Video-Surveillance System for Elder Falling Detection." In Proceedings of IEEE International Conference on Embedded Software and Systems (ICESS), HangZhou, Zhejiang, China, 25–27 May 2009; pp. 350–355.

    Google Scholar 

  31. Lee, M.W.; Nevatia, R, "Body Part Detection for Human Pose Estimation and Tracking." In Proceedings of IEEE Workshop on Motion and Video Computing (WMVC), Austin, TX, USA, 23–24 February 2007 ; pp. 23–23.

    Google Scholar 

  32. Lee, M.W.; Nevatia, R, "Human pose tracking in monocular sequence using multilevel structured models." IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31, 27–38.

    Article  Google Scholar 

  33. Lu, X.; Liu, Q.; Oe, S, "Recognizing Non-rigid Human Actions using Joints Tracking in Space-Time." In Proceedings of the IEEE International Conference on Information Technology: Coding and Computing (ITCC), Las Vegas, NV, USA, 5–7 April 2004; Volume 1; pp. 620–624.

    Google Scholar 

  34. Welch, G.; Bishop, G, "An Introduction to the Kalman Filter." In Technical Report TR 95–041; Department of Computer Science, University of North Carolina at Chapel Hill: Chapel Hill, NC, USA, 1995.

    Google Scholar 

  35. Brand, M.; Oliver, N.; Pentland, A, "Coupled hidden Markov Models for Complex Action Recognition." In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Juan, PR, USA, 17–19 June 1997; pp. 994–999.

    Google Scholar 

  36. Stauffer, C.; Grimson, W.E.L, "Learning patterns of activity using real-time tracking." IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 747–757

    Article  Google Scholar 

  37. Bodor, R.; Jackson, B.; Papanikolopoulos, N, "Vision-based Human Tracking and Activity Recognition." In Proceedings of the 11th Mediterranean Conference on Control and Automation, Rhodes, Greece, 18–20 June 2003; Volume 1, pp. 18–20.

    Google Scholar 

  38. M. Li, Z. Zhang, K. Huang, and T. Tan, “Estimating the number of people in crowded scenes by MID based foreground segmentation and head–shoulder detection,” in Proc. Int. Conf. Pattern Recog., 2008, pp. 1–4.

    Google Scholar 

  39. X. Wu, G. Liang, K. Lee, and Y. Xu, “Crowd density estimation using texture analysis and learning,” in Proc. IEEE Int. Conf. Robot. Biometrics, 2006, pp. 214–219.

    Google Scholar 

  40. L. Dong, V. Parameswaran, V. Ramesh, and I. Zoghlami, “Fast crowd segmentation using shape indexing,” in Proc. IEEE Int. Conf. Comput. Vis., 2007, pp. 1–8.

    Google Scholar 

  41. S. Park and J. K. Aggarwal, “Simultaneous tracking of multiple body parts of interacting persons,” Comput. Vis. Image Underst., vol. 102, no. 1, pp. 1–21, Apr. 2006.

    Google Scholar 

  42. I. Haritaoglu, R. Cutler, D. Harwood, and L. S. Davis, “Backpack: Detection of people carrying objects using silhouettes,” in Proc. IEEE Int. Conf. Comput. Vis., 1999, vol. 1, pp. 102–107.

    Google Scholar 

  43. Prasath, V. B., Alfeilat, H. A. A., Lasassmeh, O., and Hassanat, A. (2017). Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier-A Review. arXiv preprint arXiv:1708.04321.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and IT, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Nadeem Iqbal & Malik M. Saad Missen

  2. Department of Mathematics, Khawaja Fareed University of Engineering and Information Technology (KFUIT), Rahim Yar Khan, Pakistan

    Nadeem Salamat

  3. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    V. B. Surya Prasath

  4. Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

    V. B. Surya Prasath

Authors
  1. Nadeem Iqbal
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  2. Malik M. Saad Missen
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  3. Nadeem Salamat
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  4. V. B. Surya Prasath
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Correspondence to Malik M. Saad Missen .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, NIT Patna, Patna, India

    Amit Kumar Singh

  2. Department of Electronics Engineering, Indian Institute of Technology, BHU, Varanasi, Uttar Pradesh, India

    Anand Mohan

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Iqbal, N., Saad Missen, M.M., Salamat, N., Prasath, V.B.S. (2019). On Video Based Human Abnormal Activity Detection with Histogram of Oriented Gradients. In: Singh, A., Mohan, A. (eds) Handbook of Multimedia Information Security: Techniques and Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-15887-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-15887-3_21

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