Target tracking using wavelet features and SVM classifier

被引：0
|
作者：
Babaeean, Amir [1 ]
Tashk, Alireza Bayesteh [1 ]
Abedinee, Zahra Sadat Mir [2 ]
Barzin, Farzad [2 ]
机构：
[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran 158754413, Iran
[2] Babol Univ Techno, Dept Elect Engn, Tehran 158754413, Iran
来源：
CSNDSP 08: PROCEEDINGS OF THE SIXTH INTERNATIONAL SYMPOSIUM ON COMMUNICATION SYSTEMS, NETWORKS AND DIGITAL SIGNAL PROCESSING | 2008年
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a new method is proposed for target tracking based on wavelet transform and Support Vector Machine (SVM). Considering tracking as a classification problem, we train a SVM classifier to distinguish an object from its background. This is done by constructing feature vector for every pixel in the reference image and then training a SVM classifier to separate pixels which belong to the object from those related to the background. Receiving new video frame, SVM is employed to test the pixels and form a confidence map. In this work, the features we use the 4(th) level Daubechiess wavelet coefficients corresponding to input image. Conducting simulations, it is demonstrated that target tracking based on wavelet transform and SVM classification result in acceptable and efficient performance. The experimental results agree with the theoretical results.
引用
收藏
页码：222 / +
页数：2
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