Garima Singh Uday Pratap Singh



In this paper review analysis capturing images with high quality and good size is so easy because of rapid improvement in quality of capturing device with less costly but superior technology.  Videos are a collection of sequential images with a constant time interval. So video can provide more information about our object when scenarios are changing with respect to time. Therefore, manually handling videos are quite impossible.  So we need an automated devise to process these videos.  In this thesis one such attempt has been made to track objects in videos. Many algorithms and technology have been developed to automate monitoring the object in a video file. Object detection and tracking is a one of the challenging task in computer vision. Mainly there are three basic steps in video analysis: Detection of objects of interest from moving objects, Tracking of that interested objects in consecutive frames, and Analysis of object tracks to understand their behavior. Simple object detection compares a static background frame at the pixel level with the current frame of video. The existing method in this domain first tries to detect the interest object in video frames.  One of the main difficulties in object tracking among many others is to choose suitable features and models for recognizing and tracking the interested object from a video. Some common choice to choose suitable feature to categories, visual objects are intensity, shape, color and feature points. 


Object detection, Frame difference, Vision and scene understanding, Background subtraction,

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Yiwei Wang, John F. Doherty and Robert E. Van Dyck, ”Moving Object Tracking in Video”, in proceedings of 29th applied imagery pattern recognition workshop, ISBN 0-7695-0978-9, page 95,2000.

Bhavana C. Bendale, Prof. Anil R. Karwankar, ”Moving Object Tracking in Video Using MATLAB”, International Journal of Electronics, Communication and Soft Computing Science and Engineering ISSN: 2277-9477, Volume 2, Issue 1.

Marcus A. Brubaker, Leonid Sigal and David J. Fleet, ”Video-Based People Tracking”, hand book of ambient intelligence under smart environments 2010, pp 57-87.

Emilio Maggio and Andrea Cavallaro, ”Video Tracking: Theory and Practice”, first edition2011, John Wiley and Sons, Ltd.

Y.Alper, J.Omar, and S.Mubarak. ”Object Tracking: A Survey” ACM Computing Surveys, vol. 38, no. 4, Article 13, December 2006.

B. Triggs, P.F. McLauchlan, R.I. Hartley and A.W. ”Fitzgibbon. Bundle adjustment - a modern synthesis”. In Proceedings of the International Conference on Computer Vision, London, UK, 1999, 298?372.

G.C. Holst and T.S. Lomheim. ”CMOS/CCD Sensors and Camera Systems”. Bellingham, WA, SPIE Society of Photo-Optical Instrumentation Engineering, 2007.

E. Maggio, M. Taj and A. Cavallaro. ”Efficient multi-target visual tracking using random finite sets”. IEEE Transactions on Circuits Systems and Video Technology, 18(8), 1016?1027,2008.

G. David Lowe. Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision. 2. pp. 1150?1157,1997.

G. David Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), pp, 91-110, 2004.

Bruce D. Lucas and Takeo Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. International Joint Conference on Artificial Intelligence, pages 674-679, 1981.

Carlo Tomasi and Takeo Kanade. Detection and Tracking of Point Features. Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.

Jianbo Shi and Carlo Tomasi. Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600, 1994.

Stan Birchfield. Derivation of Kanade-Lucas-Tomasi Tracking Equation. Unpublished, January 1997.

Y. Cui, S. Samarasekera, Q. Huang. Indoor Monitoring Via the Collaboration Betweena Peripheral Senson and a Foveal Sensor, IEEE Work-shop on Visual Surveillance, Bomba y, India, 2-9, 1998.

G. R. Bradski, Computer Vision Face T racking as a Component of a Perceptual User Interface, IEEE Work. on Applic. Comp. Vis., Princeton, 214-219, 1998.

S.S. Intille, J.W. Davis, A.F. Bobick, Real-Time Closed-World T racking. IEEE Conf. on Comp. Vis. and Pat. Rec., Puerto Rico, 697-703, 1997.

C. Wren, A. Azarbayejani, T. Darrell, A. Pentland, Pfinder: Real-Time Tracking of the Human Body, IEEE Trans. Pattern Analysis Machine Intell, 19:780-785, 1997.

A. Eleftheriadis, A. Jacquin. Automatic Face Location Detection and Tracking for Model-Assisted Coding of Video Teleconference Sequences at Low Bit Rates, Signal Processing- Image Communication, 7(3): 231-248, 1995.

D. Fuiorea, V. Gui, D. Pescaru, and C. Toma. Comparative study on RANSAC and Mean shift algorithm, International Symposium on Electronics and Telecommunications Edition 8. vol. 53(67) Sept. 2008, pp. 80-85.

Y.Cheng. Mean Shift, Mode Seeking, and Clustering, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 17, No 8, 790-799,1995.

Stern H, Efros B (2005) Adaptive color space switching for tracking under varying illumination. Image Vis Comput 23(3):353364. doi : 10.1016 /j. imavis 2004.09.005

Li S-X, Chang H-X, Zhu C-F (2010) Adaptive pyramid mean shift for global real-time visual tracking. Image Vis Comput 28(3):424437. doi : 10.1016 / j. imavis 2009.06.012

Yuan G-W, Gao Y, Xu D (2011) A moving objects tracking method based on a combination of local binary pattern texture and Hue. Procedia Eng 15:39643968. doi:10.1016/j. proeng2011.08.742

Mazinan AH, Amir-Latifi A (2012) Applying mean shift, motion information and Kalman filtering approaches to object tracking. ISA Trans 51(3):485497. doi: 10.1016/j. isatra2012.02.002

Lai S-H (2004) Computation of optical flow under non-uniform brightness variations.Pattern Recognit Lett 25(8):885892. doi : 10.1016 / j. patrec 2004.02.001

Alan J Lipton, Hironobu Fujiyoshi, and Raju S Patil. Moving target classification and tracking from real-time video. In Applications of Computer Vision, 1998. WACV’98. Proceedings., Fourth IEEE Workshop on, pages 814. IEEE, 1998.

Chris Stauffer and W Eric L Grimson. Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 2.

Ya Liu, Haizhou Ai, and Guang-you Xu. Moving object detection and tracking based on background subtraction. In Multispectral Image Processing and Pattern Recognition, pages 62-66. International Society for Optics and Photonics, 2001.

Changick Kim and Jenq-Neng Hwang. Fast and automatic video object segmentation and tracking for content-based applications. Circuits and Systems for Video Technology, IEEE Transactions on, 12(2):122129, 2002.

Shahbe Mat Desa and Qussay A Salih. Image subtraction for real time moving object extraction. In Computer Graphics, Imaging and Visualization, 2004. CGIV 2004. Proceedings. International Conference on, pages 4145. IEEE, 2004.


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