Motion tracking system in video based on extensive feature set

References

• Choeychuen K., Kumhom P. and Chamnongthai K.. Robust ambiguous target handling for visual object tracking. Int. J. Electron. Commun., 2010, 64, 11.
   Web of Science ®Google Scholar
• Sivabalakrishnan M. and Manjula D.. Adaptive background subtraction in dynamic environments using fuzzy logic. Int. J. Comput. Sci. Eng., 2010, 2, 270–273.
   Google Scholar
• Huang S.-C.. An advanced motion detection algorithm with video quality analysis for video surveillance systems. IEEE Trans. Circuits Syst. Video Technol., 2011, 21, 1–14.
   Web of Science ®Google Scholar
• Tu S.-C., Chang G.-Y., Sheu J.-P., Li W. and Hsieh K.-Y.. Scalable continuous object detection and tracking in sensor networks. J. Parallel Distribut. Comput., 2010, 70, 212–224.
   Web of Science ®Google Scholar
• Uzer M. S. and Yilmaz N.. A real-time object tracking by using fuzzy controller for vision-based mobile robot. Sci. Res. Essays, 2011, 6, 4808–4820.
   Google Scholar
• Tian Y. L., Feris R. S., Liu H. W., Hampapur A. and Sun M.-T.. Robust detection of abandoned and removed objects in complex surveillance videos. IEEE Trans. Syst., 2011, 41, 565–575.
   Web of Science ®Google Scholar
• del-Blanco C. R., Jaureguizar F. and García N.. An efficient multiple object detection and tracking framework for automatic counting and video surveillance applications. IEEE Trans. Consum. Electron., 2012, 58, 857–862.
   Web of Science ®Google Scholar
• Wu H., Sankaranarayanan A. C. and Chellappa R.. Online empirical evaluation of tracking algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, 1443–1458.
   PubMed Web of Science ®Google Scholar
• Lin Y. P., Yu Q. and Medioni G.. Efficient detection and tracking of moving objects in geo-coordinates. Mach. Vis. Appl., 2011, 22, 505–520.
   Web of Science ®Google Scholar
• Youssef S. M., Hamza M. A. and Fayed A. F.. Detection and tracking of multiple moving objects with occlusion in smart video surveillance systems. IEEE Trans. Signal Process., 2010, 2, 1301–1305.
   Google Scholar
• Vishnyakov B., Vizilter Y. and Knyaz V.. Spectrum-based object detection and tracking technique for digital video surveillance. Int. Arch. Photogrammetry, 2012. pp. 692–695.
   Google Scholar
• Dong L. and Lin X.. Monocular-vision-based study on moving object detection and tracking, Proc. 4th Int. Conf. on New trends in information science and service science (NISS), Beijing, China, Jun 2010, Beijing Union University, pp. 692–695.
   Google Scholar
• Zin T. T., Tin P., Toriu T. and Hama H.. A probability-based model for detecting abandoned objects in video surveillance systems, Proc. World Cong. on Engineering, London, UK, July 2012, WCE. pp. 1936–1941.
   Google Scholar
• Sun H., Wang C. and Wang B. L.. Independently moving object detection and tracking using stereo vision, Proc. 2010 IEEE Int. Conf. on Information and automation, Harbin, China, June 2010, pp.1936–1941.
   Google Scholar
• Trucco E. and Plakas K.. Video tracking: a concise survey. IEEE J. Ocean. Eng., 2006, 31, 520–529.
   Web of Science ®Google Scholar
• Ilin R. and Deming R. W.. Simultaneous detection and tracking of multiple objects in noisy and cluttered environment using maximum likelihood estimation framework. Proc. 2010 IEEE OCEANS, Sydney, Australia, May 2010, IEEE, pp.1–7.
   Google Scholar
• Zhao X. L.. A practical technique for moving object detection and tracking, Proc. IEEE Int. Conf. on Intelligent computing and intelligent systems (ICIS), Xiamen, China, October 2010, IEEE, pp.471–474.
   Google Scholar
• Bogush R. and Maltsev S.. Minimax criterion of similarity for video information processing, Proc. IEEE Int. Conf. on Control and communications: SIBCON 2007, Tomsk, Russia, April 2007, IEEE, p. 120.
   Google Scholar
• Huang K. Q., Wang L. S., Tan T. N. and Maybank S.. A real-time object detecting and tracking system for outdoor night surveillance, Pattern Recogn., 2008, 41, 432–444.
   Web of Science ®Google Scholar
• Patel N.. Motion detection based on multi frame video under surveillance system, Int. J. Emerg. Technol. Adv. Eng., 2012, 2, 124–129.
   Google Scholar
• Fernandez J., Guerrero R., Miranda N. and Piccoli F.. Multi-level parallelism in image identification. Mecan. Comput., 2009, 28, 227–240.
   Google Scholar
• Xiao L. and Li T. Q.. Research on moving object detection and tracking, Proc. 7th Int. Conf. on Fuzzy systems and knowledge discovery, Yantai, China, August 2010, CSS, pp. 2324–2327.
   Google Scholar
• Jadav K. R., Lokhandwala M. A. and Gharge A. P.. Vision based moving object detection and tracking, Proc. Natl Conf. on Recent trends in engineering & technology, Gujarat, India, May 2011, B.V.M. Engineering College. pp. 166–169.
   Google Scholar
• Li L., Zeng X. L., Li X., Hu W. M. and Zhu P. F.. Video shot segmentation using graph-based dominant-set clustering, Proc. 1st Int. Conf. on Internet multimedia computing and service, New York, USA, November 2009, ACM. Vol. 19, No. 5, pp. 1349–1360
   Google Scholar
• Xie S. F., Shan S. G., Chen X. L. and Chen J.. Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Trans. Image Process., 2010, 19, 1349–1360.
   PubMed Web of Science ®Google Scholar
• Pun C.-M. and Wong C.-F.. Fast and robust color feature extraction for content-based image retrieval. Int. J. Adv. Comput. Technol., 2011, 3, 75–83.
   Google Scholar
• Wu C., He Y. W., Zhao L. and Zhong Y. Z.. Motion feature extraction scheme for content-based video retrieval. Proc. SPIE, 2002, 4676, 296–305.
   Google Scholar
• Anjulan A. and Canagarajah N.. Object based video retrieval with local region tracking. J. Image Commun., 2007, 22, 607–621.
   Google Scholar
• Sacchi C. and Regazzoni C. S.. A distributed surveillance system for detection of abandoned objects in unmanned railway environments. IEEE Trans. Vehicular Technol., 2000, 49, 2013–2026.
   Web of Science ®Google Scholar
• Hwang S. W., Kim E. Y., Park S. H. and Kim H. J.. Object extraction and tracking using genetic algorithm, Proc. 2001 Int. Conf. on Imaging processing, Thessaloniki, Greece, October 2001, IEEE, 383–386.
   Google Scholar
• Liu L. M., Li Z. and Delp E. J.. Efficient and low complexity surveillance video compression using backward channel aware Wyner-Ziv video coding. IEEE Trans. Circuits Syst. Video Technol., 2009, 19, 453–465.
   Web of Science ®Google Scholar
• Florez S. A. R., Frémont V., Bonnifait P. and Cherfaoui V.. An embedded multi-modal system for object localization and tracking, Intell. Transport. Syst. Mag., 2010, 4, 42–53.
   Web of Science ®Google Scholar
• Kamath C., Gezahegne A., Newsam S. and Roberts G. M.. Salient points for tracking moving objects in video. Proc. SPIE, 2005, 5695, 442.
   Google Scholar
• Yokoyama M. and Poggio T.. A contour-based moving object detection and tracking, Proc. 14th Int. Conf. on Computer communications and networks, San Diego, CA, USA, October 2005, IEEE Computer Society, pp. 271–276.
   Google Scholar
• Ianasi C., Gui V., Toma C. I. and Pescaru D.. A fast algorithm for background tracking in video surveillance, using nonparametric kernel density estimation. Electron. Energ., 2005, 18, 127–144.
   Google Scholar
• Manohar V., Soundararajan P., Raju H., Goldgof D., Kasturi R. and Garofolo J.. Performance evaluation of object detection and tracking in video, Proc. 7th Asian Conf. on Computer vision, Hyderabad, India, January 2006, IEEE, pp.151–161.
   Google Scholar