Analysis and tracking of animal movements through granulation of temporal domain (GTD)

  • Authors

    • Neelam Rawat
    • J. S. Sodhi
    • Rajesh K. Tyagi
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.21143
  • Video Surveillance, Image Granulation, Background Subtraction, Granular Computing, Temporal and Spatial Domains.

  • Thanks to technological advancement in tracking systems, observing the moving patterns of a population is easier now. Capturing the movement patterns is mostly done through various tracking technologies like GPS, GIS etc. but these tracking systems basically require one or more combination of the same, and also, a node or tag to be placed on the moving object. Tracking of movement patterns basically employs a motion model which describes how the image of the object might change in different possible positions of the object during video surveillance. To track multiple objects without deformation during movement, the granulation of temporal domain (GTD) is pro- posed.

    GTD method is for demodulation of granule adaptation of video image segmentation on the basis of granular computing methodology. In image segmentation, i.e., image granulation through temporal domain, it improves the quality of surveillance important for wild-life protec- tion, and decision making in terms of security.

     

     

  • References

    1. [1] Debarati Chakrabortya ET. al., “Granulation, rough entropy and spatio-temporal moving object detectionâ€, Applied Soft Computing 13 (2013) 4001–4009.

      [2] Nan Lu ET. al., “An Improved Motion Detection Method for Real-Time Surveillanceâ€, IAENG International Journal of Com- puter Science, 35:1, IJCS_35_1_16.

      [3] Lucas P.J.J. Noldus ET. al., “Computerised video tracking, movement analysis and behaviour recognition in insectsâ€, Com- puters and Electronics in Agriculture 35 (2002) 201–227.

      [4] Jianhui Zhao ET. al., “Contour Points based P2P Algorithm for Shape Matching and Image Retrievalâ€, Appl. Math. Inf. Sci. 8, No. 1, 37-43 (2014).

      [5] Aurelie Bugeau ET. al., “Detection and segmentation of mov- ing objects in highly dynamic scenes†IEEE Conference on Computer Vision and Pattern Recognition, CVPR’07, 2007. pp 1-7, Minneapolis.

      [6] S. Jeeva ET. al., “Survey on background modeling and fore- ground detection for real time video surveillanceâ€, 2nd Interna- tional Symposium on Big Data and Cloud Computing (ISBCC’15), Procedia Computer Science 50 ( 2015 ) 566 – 571.

      [7] Luke Bermingham ET. al., “A general methodology for n- dimensional trajectory clusteringâ€, 40957- 4174/ 2015 Elsevier Ltd.

      [8] Mrinali M. Bhajibhakare ET. al. “To Detect and Track Mov- ing Object for Surveillance Systemâ€, International Journal of Innovative Research in Computer and Communication Engi- neering Vol. 1, Issue 4, June 2013, ISSN (Print) : 2320 – 9798.

      [9] ISSN (Online): 2320 – 9801.

      [10] Alfredo Petrosino ET. al., “Rough fuzzy set-based image compressionâ€, Elsevier, Fuzzy SetsandSystems160 (2009)1485– 1506.

      [11] Adnan Khashman, “Automatic Detection, Extraction and Recognition of Moving Objectsâ€, International Journal of Sys- tems APPLICATIONS, ENGINEERING & Development Issue 1, Volume 2, 2008.

      [12] Mrinali M. Bhajibhakare ET. al., “To Detect and Track Mov- ing Object for Surveillance Systemâ€, International Journal of Innovative Research in Computer and Communication Engi- neering Vol. 1, Issue 4, June 2013.

      [13] H. Fradietal “Spatio-temporal crowd density model in a human detection and tracking frameworkâ€, Signal Processing: Image Communication 31(2015)100–111 0923-5965/& 2014 Elsevier.

      [14] C. Stasch ET. al. “Meaningful spatial prediction and aggre- gationâ€, Elsevier: Environmental Modelling & Software 51 (2014) 149e165.

      [15] Miao Lin ET. al. “Mining GPS data for mobility patterns: A surveyâ€, 2013 Elsevier B.V., Pervasive and Mobile Computing 12 (2014) 1–16.

      [16] David M. Curry “An Algorithm for Clustering Animals by Species Based upon Daily Movementâ€, ScienceDirect Procedia Computer Science 36 (2014) 629 – 636.

      [17] Lee,M.Kang “Geospatial Big Data: Challenges and Oppor- tunitiesâ€, 2214-5796/ 2015 Elsevier, Big Data Research 2 (2015) 74–81.

      [18] T.D. Pham, “Pattern recognition by active visual infor- mation processing in birdsâ€, 0020-0255/ 2014 Elsevier, Infor- mation Sciences 270 (2014) 134–142.

      [19] Hongbing Liu ET. al., “Image Superresolution Reconstruc- tion via Granular Computing Clusteringâ€, Computational Intel- ligence and Neuroscience Volume 2014, Article ID 219636/ 10.1155/2014/219636.

      [20] F. Dornaika ET. al., “Decremental Sparse Modeling Repre- sentative Selection for prototype selectionâ€, 0031-3203/ 2015, Pattern Recognition, Elsevier.

      [21] McFarlane, N.J.B. and Schofield, C.P. (1995) ‘Segmenta- tion and tracking of piglets in images’, Machine Vision and Applications, 8(3), pp. 187–193. doi: 10.1007/bf01215814.

      [22] Wren, C.R., Azarbayejani, A., Darrell, T. and Pentland,

      [23] A.P. (1997) ‘Pfinder: Real-time tracking of the human body’, IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 19(7), pp. 780–785. doi: 10.1109/34.598236.

      [24] Stauffer, C. and Grimson, W.E.L. (2000) ‘Learning patterns of activity using real-time tracking’, IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 22(8), pp. 747–757. doi: 10.1109/34.868677.

      [25] Cucchiara, R., Grana, C., Piccardi, M. and Prati, A. (2003) ‘Detecting moving objects, ghosts, and shadows in video streams’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), pp. 1337–1342. doi: 10.1109/tpami.2003.1233909.

      [26] Zheng, Y.-J. (1995) ‘Feature extraction and image segmen- tation using self-organizing networks’, Machine Vision and Applications, 8(5), pp. 262–274. doi: 10.1007/s001380050008.

      [27] Ricquebourg, Y. and Bouthemy, P. (2000) ‘Real-time track- ing of moving persons by exploiting spatio-temporal image slic- es’, IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 22(8), pp. 797–808. doi: 10.1109/34.868682.

      [28] Koutras, P. and Maragos, P. (2015) ‘A perceptually based spatio-temporal computational framework for visual saliency estimation’, Signal Processing: Image Communication, 38, pp. 15–31. doi: 10.1016/j.image.2015.08.004.

      [29] Noldus, L.P.J.J., Spink, A.J. and Tegelenbosch, R.A.J. (2001) ‘EthoVision: A versatile video tracking system for auto.

      [30] mation of behavioral experiments’, Behavior Research Methods, Instruments, & Computers, 33(3), pp. 398–414. doi: 10.3758/bf03195394.

      [31] Zheng, X., GAO, Z. and Luo, W. (2014) ‘Feature of statisti- cal projection algorithm-based image retrieval’, Applied Math- ematics & Information Sciences, 8(2), pp. 721–725. doi: 10.12785/amis/080231.

      [32] Liu, T.-L. In addition, Chen, H.-T. (2004) ‘Real-time tracking using trust-region methods’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3), pp. 397–402. doi: 10.1109/tpami.2004.1262335.

  • Downloads

  • How to Cite

    Rawat, N., S. Sodhi, J., & K. Tyagi, R. (2018). Analysis and tracking of animal movements through granulation of temporal domain (GTD). International Journal of Engineering & Technology, 7(4.5), 501-505. https://doi.org/10.14419/ijet.v7i4.5.21143