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Tracking Direction of Human Movement - An Efficient Implementation using Skeleton

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 13
Year of Publication: 2014
Merina Kundu
Dhriti Sengupta
Jayati Ghosh Dastidar

Merina Kundu, Dhriti Sengupta and Jayati Ghosh Dastidar. Article: Tracking Direction of Human Movement - An Efficient Implementation using Skeleton. International Journal of Computer Applications 96(13):27-33, June 2014. Full text available. BibTeX

	author = {Merina Kundu and Dhriti Sengupta and Jayati Ghosh Dastidar},
	title = {Article: Tracking Direction of Human Movement - An Efficient Implementation using Skeleton},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {13},
	pages = {27-33},
	month = {June},
	note = {Full text available}


Sometimes a simple and fast algorithm is required to detect human presence and movement with a low error rate in a controlled environment for security purposes. Here a light weight algorithm has been presented that generates alert on detection of human presence and its movement towards a certain direction. The algorithm uses fixed angle CCTV camera images taken over time and relies upon skeleton transformation of successive images and calculation of difference in their coordinates.


  • Dhriti Sengupta, Merina Kundu, Jayati Ghosh Dastidar, 2014. "Human Shape Variation - Efficient Implementation using Skeleton", IJACR, Vol-4, No-1, Issue-14, pg-145-150, March-2014.
  • Blum, H. 1973. "Biological shape and Visual Science", J. Theoretical Biology, Vol 38, pp 205-287.
  • Comaniciu, D. and Ramesh, V. 2000. "Robust detection and tracking of human faces with an active camera. ", IEEE International Workshop on Visual Surveillance.
  • Darrell, T. , Gordon, G. , Harwille, M. and Woodfill, J. 1998. "Integrated person tracking using stereo, color, and pattern recognition. " In CVPR, pages 601–609.
  • Haritaoglu, I. , Harwood, D. and Davis, L. 2000. "Real-time surveillance of people and their activities" PAMI, 22(8):809–830.
  • August, J. , Siddiqi, K. and Zucker, S. 1999. "Ligature Instabilities and the Perceptual Organization of Shape", Comp. Vision and Image Understanding, Vol 76, No. 3, pp 231--243.
  • Bai, X. and Latecki, L. 2008. "Path Similarity Skeleton Graph Matching", IEEE Trans. PAMI, Vol 30, No 7, 1282-1292.
  • Bai, X. , Latecki L. J. and Liu, W. -Y. 2007. "Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution", IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 29(3), pp. 449-462.
  • Zhu, S. C. and Yullie, A. L. 1996. "Forms: A Flexible Object Recognition and Modeling System", IJCV, 20(3):187–212.
  • Siddiqi, K. , Shokoufandeh, A. , Dickinson, S. and Zucker, S. 1999. "Shock graphs and shape matching", IJCV, 35(1):13–32.
  • Pizer, S. et al. 2003. "Deformable m-reps for 3d medical image segmentation", IJCV, 55(2):85–106.
  • Sebastian, T. B. , Klein, P. N. and Kimia, B. B. 2004. "Recognition of shapes by editing their shock graphs", PAMI, 26(5):550–571.
  • Macrini, D. , Siddiqi, K. and Dickinson, S. 2008. "From skeletons to bone graphs: Medial abstraction for object recognition", CVPR.
  • Song, Y. , Feng, X. and Perona, P. 2000. "Towards detection of human motion", CVPR, volume 1, pages 810–817.
  • Viola, P. , Jones, M. J. and Snow, D. 2003. "Detecting pedestrians using patterns of motion and appearance" ICCV, pages 734–741.
  • Fablet, R. and Black, M. J. 2002. "Automatic detection and trackingof human motion with a view-based representation", ECCV, volume 1, pages 476–491.
  • Cutler, R. and Davis, L. 2000. 'Robust real-time periodic motion detection: Analysis and applications", IEEE Patt. Anal. Mach. Intell. , volume 22, pages 781–796.
  • Papageorgiou, C. , Oren, M. and Poggio, T. 1998. "A general framework for object detection", International Conference on Computer Vision, 1998.
  • Isard, M. , Sullivan, J. , Blake, A. and MacCormick, J. 1999. "Object localization by bayesian correlation" ICCV, pp. 1068–1075.
  • Comaniciu, D. , Ramesh, V. and Meer, P. 2000. "Real-time tracking of non-rigid objects using mean shift", CVPR, pp. 142–149.
  • Isard, M. and MacCormick, J. 2001. "BraMBLE: a Bayesian multiple-blob tracker", ICCV, II, pp. 34–41.
  • Haritaoglu, I. , Harwood D. and Davis, L. 1999. "A real time system for detecting and tracking people", IVC.
  • Isard, M. and Blake, A. 1998. "Condensation: conditional density propagation for visual tracking", IJCV, 29(1):5–28.
  • Toyama, K. and Blake, A. 2001. "Probabilistic tracking in a metric space", ICCV, 2:50–57.