Logo PTI
Polish Information Processing Society
Logo RICE

Annals of Computer Science and Information Systems, Volume 10

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

Overview of Object Detection and Tracking based on Block Matching Techniques

, , ,

DOI: http://dx.doi.org/10.15439/2017R84

Citation: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, Vijender Kumar Solanki, Vijay Bhasker Semwal, Rubén González Crespo, Vishwanath Bijalwan (eds). ACSIS, Vol. 10, pages 313–319 ()

Full text

Abstract. Object tracking is one of the vital fields of computer vision that detects the moving object from a video sequence. Object detection is used to detect the object present in the video and to find the exact location of that object. The object tracking can be applied in various fields that include video surveillance, robot vision, traffic monitoring, automated civil or military surveillance system, traffic monitoring, human-computer interaction, vehicle navigation, biomedical image analysis, medical imaging and much more. The object tracking algorithm requires tracking the object in each frame of the video. A common approach is to use the background subtraction, which eliminates the common static background, resulting into foreground region showing the presence of the desired object. Block matching technique is the most popular technique for computing the motion vectors between the two frames of video sequences and different searching techniques are available to compute motion vectors between frames. Still, there is a scope for improvement in modifying or developing a new shape pattern for block matching motion estimation to find out and track the object in the video. This paper presents the several object detection and tracking methods and how block matching can be used to track object from a video.

References

  1. H. S. Parekh, U.K. Jaliya, D.G. Thakore, “A Survey on Object Detection and Tracking Methods”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 2, 2014.
  2. S. H. Shaikh, N.Chaki, K. Saeed, “Moving Object Detection Using Background Subtraction”, Springer Briefs in Computer Science, 2014.
  3. A. Yilmaz, O. Javed, M. Shah, “Object Tracking: A Survey”, ACM Computer Survey 38, Vol. 4, Article 13, 2006.
  4. B. Karasulu. and S. Korukoglu, “Moving Object Detection and Tracking in Videos”, Springer Briefs in Computer Science, 2013.
  5. K. U. Sharma and N.V. Thakur, “A Review and an Approach for Object Detection in Images”, International Journal of Computational Vision and Robotics, Inderscience Publisher, 2014.
  6. Helly, M. Desai, V. Gandhi, “A Survey: Background Subtraction Techniques”, International Journal of Scientific & Engineering Research, Vol.5, Issue 12, 2014.
  7. M. Piccardi, “Background subtraction techniques: a review”, IEEE International Conference on Systems, Man and Cybernetics, 2004.
  8. I. Cohen and G.Medioni, “Detecting and Tracking Moving Objects for Video Surveillance”, IEEE Proc. Computer Vision and Pattern Recognition Jun. 23-25, 1999.
  9. N. Singla, “Motion Detection Based on Frame Difference Method”, International Journal of Information & Computation Technology, Vol. 4, Issue 15, 2014.
  10. J. S. Kulchandani and K. J. Dangarwala, “Moving Object Detection: Review of Research Trends”, International Conference on Pervasive Computing, 2015.
  11. R. Zhang, J. Ding, “Object Tracking and Detecting Based on Adaptive Background Subtraction”, International Workshop on Information and Electronics Engineering, 2012.
  12. Duc Phu Chau, Francois Bremond, Monique Thonnat, “Object Tracking in Videos: Approaches and Issues”, the International Workshop "Rencontres UNS-UD", 2013.
  13. A. K. Chauhan and D. Kumar, “Study of Moving Object Detection and Tracking for Video Surveillance”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, Issue 4, 2013.
  14. R. A. Hadi, G. Sulong, and L.E. George, “Vehicle Detection and Tracking Techniques: A Concise Review”, Signal & Image Processing: An International Journal (SIPIJ) Vol.5, Issue.1, 2014.
  15. A. Gyaourova, C. Kamath, S.-C. Cheung, “Block matching for object tracking”, Lawrence Livermore National Laboratory, 2003.
  16. A. Barjatya, “Block Matching Algorithms for Motion Estimation”, DIP 6620 Spring 2004 Final Project Paper, 2004.
  17. H. A. Surrah, Mohd. J. Haque, “A Comparative Approach for Block Matching Algorithms used for Motion Estimation”, International Journal of Computer Science Issues, Vol. 11, Issue 3, No. 2, 2014.
  18. Hsiang-Kuo Tang, Tai-Hsuan Wu, Ying-Tein Lin, “Real-time object Image Tracking based On Block Matching Algorithm”, Project Report available at https://homepages.cae.wisc.edu/~ece734/project/s06/lintangwuReport.pdf
  19. P. C. Shenolikar, S. P. Narote, “Different Approaches for Motion Estimation”, International Conference On Control, Automation, Communication and Energy Conservation, 2009.
  20. M. H. Sherie, I. Ashimaa, Mahmoud Imbaby and A. Elam, “Experimental Comparison among Fast Block Matching Algorithms (FBMAs) For Motion Estimation and Object Tracking”, in Proceeding of 28th National Radio Science Conference (NRSC 2011),National Telecommunication Institute, Egypt, 2011.
  21. Sharif Abd Elohim, “An Efficient Object Tracking Technique Using Block - Matching Algorithm”, Nineteenth National Radio ScienceConference, Alexandria, 2002.
  22. Budi Sugandi, Hyoungseop Kim., Joo Kooi Tan and Seiji Ishikawa, “A Block Matching Technique for Object Tracking”, 2008 International Conference on Computer and Communication Engineering, Kuala Lumpur, 2008.
  23. S. Zhu and K.K Ma, “A New Diamond Search Algorithm for Fast Block-Matching Motion Estimation,” IEEE Transaction on image processing, Vol. 9, Issue.2, 2000.
  24. Wei Liu, Xiujuan Sun, Huai Yuan,“Moving target tracking based on the improved diamond search algorithm and Gaussian scale-space”, Third International Conference on Intelligent Human-Machine Systems, 2011.
  25. C. H. Cheung and L.M.Po, “A Novel Cross-Diamond Search Algorithm for Fast Block Motion Estimation,” IEEE transaction on circuit and system for video technology, Vol. 12, NO. 12, 2002.
  26. N. Verma, T. Sahu, P.Sahu, “Efficient Motion Estimation by Fast Three Step Search Algorithms”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 1, Issue 5, 2012.
  27. L. M. Po and W.C. Ma, “A Novel Four-Step Search Algorithm for Fast Block Motion Estimation,” IEEE transaction on circuit and system for video technology, Vol. 6, NO. 3, .1996.
  28. S. D. Kamble, N. V. Thakur, and P. R. Bajaj, “A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding,” International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, No.2, 2016.
  29. R. Venkatesh Babu, Patrick Perez, Patrick Bouthemy, “Robust tracking with motion estimation and local Kernel-based color modeling”, Image and Vision Computing Elsevier, 2007.
  30. R Li, B.Zeng, and M.L. Liou, “A New Three-Step Search Algorithm for Block Motion Estimation”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 4, No. 4, 1994.
  31. K. Hariharkrishnan and Dan Schonfeld, “Fast Object Tracking Using Adaptive Block Matching”, IEEE Transactions on Multimedia, Vol. 7, No. 5, 2005.
  32. S. D. Kamble, N. V. Thakur, L.G. Malik and P. R. Bajaj, “Fractal Video Coding Using Modified Three-step Search Algorithm for Block- matching Motion Estimation”, Computational Vision and Robotics Proceedings of International Conference on Computer Vision and Robotics, (ICCVR’14), Advances in Intelligent Systems and Computing, Vol. 332, Springer-India, 2015.
  33. S. D. Kamble, N. V. Thakur, L. G. Malik and P. R. Bajaj, “Color video compression based on fractal coding using quad-tree weighted finite automata”, Information system design and intelligent application, Proceedings of 2nd International Conference INDIA 2015,Vol.2, Advances in intelligent system and computing, Springer India, Vol. 340, 2015.
  34. S. D. Kamble, N.V. Thakur, L. G. Malik and P. R. Bajaj, “Quad tree partitioning andextended weighted finite automata-based fractal color video coding”, Int. J.Image Mining, Vol. 2, No. 1, pp.31–56, 2016.
  35. S. Acharjee, G. Pal, T. Radha, S. Chakraborty, S. Chaudhuri. S, and N. Dey, “Motion vector estimation using parallel processing”, IEEE International Conference on Circuits, Communication, Control and Computing (I4C), 2014.
  36. S. Acharjee, N. Dey, D. Biswas, P. Das, and S. Chaudhuri,“A novel Block Matching Algorithmic Approach with smaller block size for motion vector estimation in video compression”, 12 thIEEE International Conference on Intelligent Systems Design and Applications (ISDA), 2012.
  37. S. Acharjee, D. Biswas, N. Dey, P. Maji, and S. Chaudhuri,“An efficient motion estimation algorithm using division mechanism of low and high motion zone”, IEEE International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013.