A survey on 2d object tracking in digital video
This paper presents object tracking methods in video.Different algorithms based on rigid, non rigid and articulated object tracking are studied. The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends.It is often...
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my.uum.repo.134802015-04-01T06:28:09Z http://repo.uum.edu.my/13480/ A survey on 2d object tracking in digital video Khobragade, A.S. Dethe, C.G. Kulat, K.D. QA76 Computer software This paper presents object tracking methods in video.Different algorithms based on rigid, non rigid and articulated object tracking are studied. The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends.It is often the case that tracking objects in consecutive frames is supported by a prediction scheme. Based on information extracted from previous frames and any high level information that can be obtained, the state (location) of the object is predicted.An excellent framework for prediction is kalman filter, which additionally estimates prediction error.In complex scenes, instead of single hypothesis, multiple hypotheses using Particle filter can be used.Different techniques are given for different types of constraints in video. 2009-06-24 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/13480/1/PID66.pdf Khobragade, A.S. and Dethe, C.G. and Kulat, K.D. (2009) A survey on 2d object tracking in digital video. In: International Conference on Computing and Informatics 2009 (ICOCI09), 24-25 June 2009, Legend Hotel, Kuala Lumpur. http://www.icoci.cms.net.my |
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QA76 Computer software Khobragade, A.S. Dethe, C.G. Kulat, K.D. A survey on 2d object tracking in digital video |
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This paper presents object tracking methods in video.Different algorithms based on rigid, non rigid and articulated object tracking are studied. The goal of this article is to review the state-of-the-art tracking methods, classify them
into different categories, and identify new trends.It is often the case that tracking objects in consecutive frames is supported by a prediction scheme. Based on information extracted from previous frames and any high level information that can be obtained, the state (location) of the
object is predicted.An excellent framework for prediction is kalman filter, which additionally estimates prediction error.In complex scenes, instead of single hypothesis, multiple hypotheses using Particle filter can be used.Different
techniques are given for different types of constraints in video. |
format |
Conference or Workshop Item |
author |
Khobragade, A.S. Dethe, C.G. Kulat, K.D. |
author_facet |
Khobragade, A.S. Dethe, C.G. Kulat, K.D. |
author_sort |
Khobragade, A.S. |
title |
A survey on 2d object tracking in digital video |
title_short |
A survey on 2d object tracking in digital video |
title_full |
A survey on 2d object tracking in digital video |
title_fullStr |
A survey on 2d object tracking in digital video |
title_full_unstemmed |
A survey on 2d object tracking in digital video |
title_sort |
survey on 2d object tracking in digital video |
publishDate |
2009 |
url |
http://repo.uum.edu.my/13480/1/PID66.pdf http://repo.uum.edu.my/13480/ http://www.icoci.cms.net.my |
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1644281195765170176 |
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13.209306 |