Proceedings of the 2017 7th International Conference on Manufacturing Science and Engineering (ICMSE 2017)

Application of Dual DSP Target Tracking System Based on SVM

Authors
Xinghong Li, Qian Xiang, Jinfa Cheng
Corresponding Author
Xinghong Li
Available Online April 2017.
DOI
10.2991/icmse-17.2017.28How to use a DOI?
Keywords
Dual DSP, target tracking, the SVM, automatic detection
Abstract

With the real-time imaging tracking technology is widely used in various fields,military response to real-time tracking system,the precision and rapidity requirements are also increasing.This system for moving object in video monitoring field,designed target tracking system based on TMS320C6455 of dual DSP.At the same time,because the target rotating shade,dimension change,deformation and the change of light caused by low tracking precision of faults for the traditional target tracking system,the system applied the statistical learning theory of support vector machine (SVM) to the target tracking system. Practice has proved that the algorithm in the target tracking of dual DSP platform can detect and track field or predefined target in the image automatically, it overcomes the defect of traditional target tracking system.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 7th International Conference on Manufacturing Science and Engineering (ICMSE 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-327-2
ISSN
2352-5401
DOI
10.2991/icmse-17.2017.28How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xinghong Li
AU  - Qian Xiang
AU  - Jinfa Cheng
PY  - 2017/04
DA  - 2017/04
TI  - Application of Dual DSP Target Tracking System Based on SVM
BT  - Proceedings of the 2017 7th International Conference on Manufacturing Science and Engineering (ICMSE 2017)
PB  - Atlantis Press
SP  - 151
EP  - 155
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-17.2017.28
DO  - 10.2991/icmse-17.2017.28
ID  - Li2017/04
ER  -