A Review on Segmentation of Moving Object Under Dynamic Condition

Authors

  • L Kala Computer Science and Engineering, R V College of Engineering, India
  • R Hemavathy Computer Science and Engineering, R V College of Engineering, India

Keywords:

Data Mining, Image Mining, Clustering, Hierarchical Clustering, Cheameleon, Data Base, Image Retreival

Abstract

Detecting and segmenting moving objects in dynamic scenes is a hard but essential task in a number of applications such as surveillance. Most existing methods only give good results in the case of persistent or slowly changing background, or if both the objects and the background are rigid. It is a difficult task to segment the moving object in the existence of dynamic background. Different kinds of methods exist in this paper to solve the problem of motion detection and motion segmentation. This paper tells us about the various methods used to detect objects under dynamic conditions, where each method is explained in brief along with its merits and demerits.

References

Aurelie Bugeau Patrick Perez, "Detection and segmentation of moving objects in highly dynamic scenes", Proc. Int. Conf. Computer Vision.

Andrea Cavallaro and Touradj Ebrahimi , "Accurate Video Object Segmentation Through Change Detection", Proc. Int. Conf .Signal Processing Institute.

Yaakov Tsaig and Amir Averbuch,"Automatic Segmentation of Moving Objects in Video Sequences: A Region Labeling Approach" IEEE Transactions On Circuits And Systems For Video Technology, Vol. 12, No. 7, July 2002.

Rita Cucchiara, Costantino Grana, Massimo Piccardi, and Andrea Prati,"Detecting Moving Objects, Ghosts, and Shadows in Video Streams"IEEE Transactions On Pattern Analysis And Machine Intelligence,Vol. 25, No. 10, October 2003.

Gagandeep Kaur"Detection of Moving Objects in Colour based and Graph"s axis Change method" International Journal of Computing & Business Research.

Chang Liua, PongC.Yuena,GuopingQiu, "Object motion detection using information theoretic spatio-temporal saliency ", published in Elsevier journal 2009.

Tao Yang , Stan Z.Li , Quan Pan, Jing Li "Real-Time and Accurate Segmentation of Moving Objects in Dynamic Scene", published in ACM transactions.

R.Cucchiara, C.Grana, M.Piccardi, A.Prati, "Statistic and Knowledge-based Moving Object Detection in Traffic Scenes".

Vijay Mahadevan and Nuno Vasconcelos "Unsupervised Moving Target Detection In Dynamic Scenes".

Bohyung Han, Larry S. Davis, Fellow, "Density-Based Multi-Feature Background Subtraction with Support Vector Machine" IEEE Transactions On Pattern Analysis And Machine Intelligence.

Feng Liu and Michael Gleicher, "Learning color and locality cues for moving object detection and segmentation".

Amit K Agrawal and Rama Chellappa "Moving object segmentation and dynamic scene reconstruction using two frames".

Yuanlu Xu, Liang Lin "Moving object segmentation by pursuing local spatio-temporal manifolds" April 17, 2013

Ivan Laptev, Serge J. Belongie, Patrick P"erez and Josh Wills, "Periodic Motion Detection and Segmentation via Approximate Sequence Alignment".

Jinglan Li,"Moving Objects Segmentation Based on Histogram for Video Surveillance", published in Modern Applied Science Vol. 3, No. 11, November, 2009.

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Published

2013-11-30

How to Cite

[1]
L. Kala and R. Hemavathy, “A Review on Segmentation of Moving Object Under Dynamic Condition”, Int. J. Comp. Sci. Eng., vol. 1, no. 3, pp. 26–31, Nov. 2013.

Issue

Vol. 1 No. 3 (2013): IJCSE November Edition

Section

Review Article

License

Copyright (c) 2025 L. Kala, R. Hemavathy

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