Neurofuzzy prediction for visual tracking
Real time visual tracking is a complicated problem due the different dynamic of the objects involved in the process. On one hand the algorithms for image processing usually consume a lot of time on the other hand the motors and mechanisms used for the camera movements are significantly slow. This work describes the use of ANFIS model to reduce the delays effects in the control for visual tracking and also explains how we resolved this problem by predicting the target movement using a neurofuzzy approach.
ANFIS prediction visual tracking Hybrid learning rule
Mana Tarjoman Vina Tarjoman
Engineering Faculty Islamic Azad University (Abhar branch) Zanjan, Iran Management Faculty University of Tehran Tehran, Iran
国际会议
上海
英文
363-367
2010-06-22（万方平台首次上网日期，不代表论文的发表时间）