header search
Most read research Articles

Call for Papers - March 2017 Edition

International Journal of Computer Applications Technology and Research (IJCATR) call for research paper for Volume 6 Issue 3 March 2017 Edition. Submit manuscript to editor@ijcat.com. Last date of manuscript submission is February 28, 2017.



International Journal of Computer Applications Technology and Research (IJCATR)

Volume 3 Issue 5 May 2014

Moving Object Detection with Fixed Camera and Moving Camera for Automated Video Analysis

Dipali Shahare, Ranjana Shende




Keywords: Object Detection, Soft Impute method, Markov Random Field, Temporal Differencing, Moving object extraction, background subtraction.

Abstract References BibText Highlights

        Detection of moving objects in a video sequence is a difficult task and robust moving object detection in video frames for video surveillance applications is a challenging problem. Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Frequently, an object detector requires manual labeling, while background subtraction needs a training sequence. To automate the analysis, object detection without a separate training phase becomes a critical task. This paper presents a survey of various techniques related to moving object detection and discussed the optimization process that can lead to improved object detection and the speed of formulating the low rank model for detected object.

  1. N. Paragios, and R. Deriche.. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Patt. Analy. Mach. Intell. 22, 3, 266–280, 2000.
  2. A Survey on Moving Object Detection and Tracking in Video Surveillance System Kinjal A Joshi, Darshak G. Thakore International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012.
  3. C. Stauffer and W. Grimson. Adaptive background mixture models for realtime tracking. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, page 246252, 1999.
  4. Sumer Jabri, Zoran Duric, Harry Wechsler, Azriel Rosenfeld, “Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information,” In Proc. 15th Int’l Conf. on Pattern Reg, 2000,vol. 4,pp. 627 – 630.
  5. James W. Davis, Stephanie R. Taylor, “Analysis and Recognition of Walking Movements,” In Proc.16th Int’l Conf. on pattern Recognition, 2002, vol.1, pp. 315 – 318.
  6. I. Haritaoglu, D. Harwood, and L. Davis, “W4: Who? When? Where? What? A real time system for detecting and tracking people”. In Proc. Int. Conf. Auto. Face and Gesture Recog., 1998, pages 222– 227.
  7. A Unified Approach to Moving Object Detection in 2D and 3D Scenes Michal Irani and P. Anandan IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 6, JUNE 1998 577.
  8. Shireen Y. Elhabian, Khaled M. El-Sayed and Sumaya H. Ahmed,” Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art”, Recent Patents on Computer Science, 2008.
  9. Xiaowei Zhou,Can Yang, and Weichuan Yu,” Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation”, IEEE TRANSACTIONS ON PATTERN ANLYSIS AND MACHINE INTELLIGENCE, IEEE, March 2013.
  10. R. Mazumder, T. Hastie, and R. Tibshirani, “Spectral Regularization Algorithms for Learning Large Incomplete atrices,” J. Machine Learning Research, vol. 11, pp. 2287-2322, 2010.
  11. A. Yilmaz, O. Javed, and M. Shah, “Object Tracking: A Survey,” ACM Computing Surveys, vol. 38, no. 4, pp. 1-45, 2006.
  12. Ding Zhonglin and Lili,”Research on hybrid Moving Object Detection Algorithum in
  13. Yoginee B. Bramhe(Pethe), P.S. Kulkarni, “An Implementation of Moving Object Detection,Tracking and Counting Objects for Traffic Surveillance System,” Int’l Conf. on Computational Intelligence and Comm. Networks (CICN), 2011, pp. 143 – 148.
  14. Imamura.K, Kubo.N, Hashimoto.H,"Automatic moving object extraction using x-means clustering Picture Coding Symposium (PCS),pp246 - 249 , Dec 2010.
  15. R. Li, S. Yu, and X. Yang, "Efficient spatio-temporal segmentation for extract ing moving objects in video sequences," IEEE Transactions on Consumer Electronics, vol. 54, pp. 1161-1 167, Mar 2007 .
  16. A. M. McIvor. “Background subtraction techniques”, In Proc. of Image and Vision Computing, Auckland, New Zealand, 2000.
  17. D. Sappa, Fadi Dornaika, David Geronimo Antonio Lopez.“Registration Based moving object detection from a moving camera “,IROS 2008 2nd Workshop: Planning , Perception and Navigation for Intelligent Vehicles.

title = "Moving Object Detection with Fixed Camera and Moving Camera for Automated Video Analysis ",
journal = "International Journal of Computer Applications Technology and Research",
volume = "3",
number = "5 ",
pages = "277 - 283",
year = "2014",
author = "Dipali Shahare, Ranjana Shende ",

The paper proposes an approach in which integrates object detection and background subtraction into a single process of optimization for speedup the computation.
To improve the accuracy detecting the object in video and cut down the cost of computations using the process of optimization.
The objective is to handle static and dynamic background while process the video.
In this paper, later will work on outliers present in video, in this it detects and removes outlier (i.e. noise) present in sequence of frames.