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Extracting traffic information using...

University of California, Davis.

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Extracting traffic information using video image processing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Extracting traffic information using video image processing./
作者:	Singh, Shailendra Pratap.
面頁冊數:	44 p.
附註:	Adviser: Michael Zhang.
Contained By:	Masters Abstracts International47-05.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1463452
ISBN:	9781109082807

Extracting traffic information using video image processing.
Singh, Shailendra Pratap.

Extracting traffic information using video image processing. - 44 p.

Adviser: Michael Zhang.

Thesis (M.S.)--University of California, Davis, 2008.

This thesis presents a methodology for extracting traffic parameter using video image processing. It is an offline system that focuses on extracting vehicles, tracking them and provides the vehicle count for a short period of time. It uses background subtraction algorithm for extracting moving objects. The salient feature of this research is that it uses a novel method for extracting vehicle count. The proposed method for counting vehicles uses screen-line that keeps track of each vehicle crossing it. The results show that the algorithm is capable of counting 100% of the vehicles. Some noise was induced during the cast shadow removal process. Due to the presence of such noise pixels, final vehicle counts were increased by approximately 15%.

ISBN: 9781109082807Subjects--Topical Terms:

626642
Computer Science.

Extracting traffic information using video image processing.
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245 1 0 $a Extracting traffic information using video image processing.
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502 $a Thesis (M.S.)--University of California, Davis, 2008.
520 $a This thesis presents a methodology for extracting traffic parameter using video image processing. It is an offline system that focuses on extracting vehicles, tracking them and provides the vehicle count for a short period of time. It uses background subtraction algorithm for extracting moving objects. The salient feature of this research is that it uses a novel method for extracting vehicle count. The proposed method for counting vehicles uses screen-line that keeps track of each vehicle crossing it. The results show that the algorithm is capable of counting 100% of the vehicles. Some noise was induced during the cast shadow removal process. Due to the presence of such noise pixels, final vehicle counts were increased by approximately 15%.
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