Video Object Tracking Based on Active Contour Model and Color Information Classification

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题名:	Video Object Tracking Based on Active Contour Model and Color Information Classification
作者:	Yuan-Lei Chao
贡献者:	Department of Computer Science and Information Engineering
关键词:	active contour model;GVF snake;k-means clustering;block matching;video object tracking
日期:	2010
上传时间:	2010-04-21 08:57:51 (UTC+0)
出版者:	Asia University
摘要:	This thesis presents a video object tracking system based on the active contour model and color information classification. First, the active contour model is applied to detect the target object and the background regions in the initial frame. The pixels of object and the background regions are clustered according to their color information using the k-means clustering algorithm. Then the video objects in the subsequent frames are automatically detected and tracked using temporal differencing and block matching. The moving and stationary regions in a frame are estimated by the temporal differencing. In the moving regions, pixels are obtained their classification from the previous frame using block matching while they are directly received their classification from the previous frame in the stationary regions. Finally, a post-processing procedure is applied to correct the error pixels. Experimental results show that the proposed method provides better performance than the active contour method in video object tracking.
显示于类别:	[資訊工程學系] 博碩士論文

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