KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Human Tracking Based On Context Awareness In Outdoor Environment

  • Binh, Nguyen Thanh (Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, VNU-HCM) ;
  • Khare, Ashish (Department of Electronics and Communication University of Allahabad) ;
  • Thanh, Nguyen Chi (Faculty of Electronics and Computer Science Engineering Cao Thang Technical College)
  • Received : 2016.05.10
  • Accepted : 2017.03.30
  • Published : 2017.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.

Keywords

References

  1. Erdem C. E., Tekalp A. M., Sankur B., "Video object tracking with feedback of performance measures," IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no 4, pp 310-324, 2003 https://doi.org/10.1109/TCSVT.2003.811361
  2. Liu T. L., Chen H. T., "Real-time tracking using trust region methods," IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 26, no 3, pp 397-402, 2004. https://doi.org/10.1109/TPAMI.2004.1262335
  3. Wang D., "Unsupervised video segmentation based on watersheds and temporal tracking," IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no 5, pp 539-546, 1998. https://doi.org/10.1109/76.718501
  4. Zhou S. K., Chellappa R. , Moghaddam B., "Visual tracking and recognition using appearance-adaptive models in particle filters," IEEE Transactions on Image Processing, vol. 13, no 11, pp 1491-1506, 2004. https://doi.org/10.1109/TIP.2004.836152
  5. Comaniciu D., Ramesh V. , Meer P., "Kernel-based object tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no 5, pp 564-577, 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  6. Comaniciu D., Meer P., "Mean shift: a robust approach toward feature space analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no 5, pp 603-619, 2002. https://doi.org/10.1109/34.1000236
  7. Zivkovic Z. , Krose B., "An EM-like algorithm for color histogram- based object tracking," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA, pp 798-803, 2004.
  8. Yilmaz A., Javed O., Shah M., "Object Tracking: A survey," ACM Computing Surveys, vol. 38, no 4, 2006.
  9. Wang D., "Unsupervised video segmentation based on watersheds and temporal tracking," IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no 5, pp 539-546, 1998. https://doi.org/10.1109/76.718501
  10. Moeslund T.B., Granum E., "A survey of computer vision based human motion capture," Computer Vision and Image Understanding, vol. 81, pp 231-268, 2001. https://doi.org/10.1006/cviu.2000.0897
  11. Ruolin Zhang, Jian Ding, "Object tracking and detecting based on adaptive background subtraction," in Proc. of International workshop on information and electronics engineering, Vol. 29, pp1351-1355, 2012.
  12. Wei Shuigen Chen Zhen, Dong Hua, "Motion detection based on temporal difference method and optical flow field," in Proc. of Second international symposium on electronic commerce and security, vol. 2, pp 85-88, 2009.
  13. Masafumi S., Thi T. Z., Takashi T., Shigeyoshi N, "Robust rule-based method for human activity recognition," International journal of computer science and network security, vol.11, no.4, pp. 37-43, 2011.
  14. Johnsen, S., Tews, A, "Real-time object tracking and classification using a static camera," in Proc. of International Conference on Robotics and Automation, workshop on People Detection and Tracking, 2009.
  15. Stauffer, C., Grimson, W. E. L, "Adaptive background mixture models for real-time tracking," in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 1999.
  16. Andrzej Glowacz, Zbigniew Mikrut, Piotr Pawlik, "Video Detection Algorithm Using an Optical Flow Calculation Method," in Proc. of 5th International Conference on Multimedia Communications, Services and Security, pp 118-129, 2012
  17. Peng Dai, Linmi Tao, Guangyou Xu, "Dynamic context driven human detection and tracking in meeting scenarios," in Proc. of 2nd International Conference on Computer Vision Theory and Applications, Volume Special Session, pp 31-38, 2007.
  18. Qi Zang, Reinhard Klette, "Object classification and tracking in video surveillance," Computer Analysis of Images and Patterns, vol. 2756, pp. 198-205, 2003.
  19. Comaniciu D., Ramesh V., Meer P., "Kernel based object tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 25, no 5, pp 564-577, 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  20. Nummiaro K., Meier E. K., Gool L.J.V., "An Adaptive Color-based Particle Filter," Image and Vision Computing," vol. 21, pp 99-110, 2003. https://doi.org/10.1016/S0262-8856(02)00129-4
  21. Nguyen Thanh Binh, A.Khare, "Object tracking of video sequences in curvelet domain," International Journal of Image and Graphics, vol. 11, no 1, pp 1-20, 2011. https://doi.org/10.1142/S0219467811003968
  22. Nguyen Thanh Binh, T.A.Dien, "Object detection and tracking in contourlet domain," in Proc. of the First International Conference on Context-Aware Systems and Applications, Viet Nam, vol. 109, pp 192-200, 2012.
  23. Gitta Kutyniok, Demetrio Labate, "Shearlets: multiscale analysis for multivariate data," Birkhauser, ISBN 0817683151 9780817683153, 2012.
  24. Wang-Q Lim, "The discrete shearlet transform: a new directional transform and compactly supported shearlet frames," IEEE Transactions on image processing, vol 19, no 5, pp 1166 - 1180, 2010. https://doi.org/10.1109/TIP.2010.2041410
  25. K. Guo, D. Labate, "Optimally sparse multidimensional representation using shearlets," SIAM J. Math. Anal., vol. 39, pp. 298-318, 2007. https://doi.org/10.1137/060649781
  26. Nguyen Thanh Binh, "Vehicle tracking in outdoor environment based on curvelet domain," in Proc. of the International Conference on Nature of Computation and Communication, Viet Nam, vol. 144, pp 360-369, 2014.
  27. Vishal M. Patel, Glenn R. Easley, Dennis M. Healy, "Shearlet-based deconvolution," IEEE Transactions on Image Processing, vol. 18, no. 12, pp 2673-2685, 2009. https://doi.org/10.1109/TIP.2009.2029594
  28. Viola, P. & Jones, M, "Rapid object detection using a boosted cascade of simple feature," in Proc. of International Conference on Computer Vision and Pattern Recognition, pp 83-87, 2001
  29. Zhu, Z., Zou, H., Rosset, S., Hastie, T, "Multiclass adaboost," International Journal of Statistics and its Interface, pp 349-360, 2009.
  30. Renno, J. P., Makris, D., Jones, G. A, "Object classification in visual surveillance using adaboost," in Proc. of International Conference on Computer Vision and Pattern Recognition, pp 1-8, 2007.
  31. M.Khare, Nguyen Thanh Binh, R.K. Srivastava, A. Khare, "Vehicle identification in traffic surveillance - complex wavelet transform based approach," Journal of Science and Technology, vol 52, no 4A, pp 29-38, 2014.
  32. Castleman K. R., "Digital Image Processing," Prentice Hall, Englewood Cliffs, USA, 1996.
  33. Manish Khare, Nguyen Thanh Binh, Rajneesh Kumar Srivastava, "Human object classification using dual tree complex wavelet transform and zernike moment," Transactions on Large-Scale Data- and Knowledge-Centered Systems XVI, pp 87-101, 2014.
  34. Zhihua, Li , Fan Zhou , Xiang Tian, Yaowu Chen, "High efficient moving object extraction and classification in traffic video surveillance," Journal of Systems Engineering and Electronics, vol. 20, no 4, pp. 858-868, 2009. https://doi.org/10.1109/JSEE.2009.6074512
  35. Nguyen Thanh Binh, "Human object detection based on context awareness in the surroundings," EAI Endorsed Transactions on Context-aware Systems and Applications, vol. 2, issue 4, 2015.
  36. Tang Sze Ling, Liang Kim Meng, Lim Mei Kuan, Zulaikha Kadim, Ahmed A. Baha Al-Deen, "Colour-based object tracking in surveillance application," in Proc. of International Multi Conference of Engineers and Computer Scientists, vol. 1, 2009.
  37. PESTS2009 : http://ftp.pets.rdg.ac.uk/pub/PETS2009/
  38. CAVIAR: http://groups.inf.ed.ac.uk/vision/caviar/
  39. SUN: http://vision.princeton.edu/projects/2010/SUN/
  40. Lucas, B.D Kanade T. "An iterative image registration technique with an application to stereo vision," in Proc. of Imaging Understanding Workshop, pp 121-130, 1981.
  41. Tsuchiya M, Fujiyoshi H, "Evaluating feature importance for object classification in visual surveillance," in Proc. of The 18th IEEE International Conference on Pattern Recognition, pp. 978-981, 2006.
  42. Zhang T, Ghanem B, Liu S, Ahuja N, "Robust visual tracking via structured multi-task sparse learning," Journal of Computer Vision, vol. 101, pp 367-383, 2013 https://doi.org/10.1007/s11263-012-0582-z
  43. Zhong W, Lu H, Yang MH, "Robust object tracking via sparsity-based collaborative model," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1838-1845, 2012
  44. Manish Khare, Rajneesh Kumar Srivastava, Ashish Khare, "Object tracking using combination of daubechies complex wavelet transform and zernike moment," Multimedia Tools and Applications, doi:10.1007/s11042-015-3068-5, pp 1-44, 2015.