Adaptive particle sampling and adaptive appearance for multiple video object tracking

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29098

Title:	Adaptive particle sampling and adaptive appearance for multiple video object tracking
Authors:	Cheng,HY;Hwang,JN
Contributors:	資訊工程研究所
Keywords:	FILTERS;MODELS
Date:	2009
Issue Date:	2010-06-29 20:13:41 (UTC+8)
Publisher:	中央大學
Abstract:	In this work, we propose an innovative method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking. Taking advantage of both the closed-form equations for optimal prediction and update from Kalman filters and the versatility of particle sampling for measurement selection under occlusion or segmentation error cases, the proposed method achieves both high tracking accuracy and computational simplicity. The adaptive particle sampling, which uses parameters updated by Kalman filters, can thus require only a small number of particles to achieve high positioning and scaling accuracy. Also, the concept of adaptive appearance is applied to enhance the robustness of occlusion handling. The experimental results confirm the effectiveness of the proposed method. (C) 2009 Elsevier B.V. All rights reserved.
Relation:	SIGNAL PROCESSING
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

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