Target Tracking Feature Selection Algorithm Based on Adaboost

Chen Yi

Abstract


With the development of image processing technology and popularization of computer technology, intelligent machine vision technology has a wide range of application in the medical, military, industrial and other fields. Target tracking feature selection algorithm is one of research focuses in the machine intelligent vision technology. Therefore, to design the target tracking feature selection algorithm with high accuracy and good stability is extremely necessary. This paper presents a target tracking feature selection algorithm based on Adaboost. It includes Adaboost algorithm’s principle and Adaboost algorithm's application in video object tracking. Experimental results show that the proposed algorithm has the characteristics of real-time, accuracy and stability.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3688


Keywords


Data mining; University financial analysis; Clustering algorithm; Financial early warning

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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