文章基本信息

标题：Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker
本地全文：下载
作者：Fitri Utaminingrum ; Yuita Arum Sari ; Putra Pandu Adikara 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2018
卷号：16
期号：4
页码：1616-1624
DOI：10.12928/telkomnika.v16i4.6595
语种：English
出版社：Universitas Ahmad Dahlan
其他摘要：Disability may limit someone to move freely, especially when the severity of the disability is high. In order to help disabled people control their wheelchair, head movement-based control is preferred due to its reliability. This paper proposed a head direction detector framework which can be applied to wheelchair control. First, face and nose were detected from a video frame using Haar cascade classfier. Then, the detected bounding boxes were used to initialize Kernelized Correlation Filters tracker. Direction of a head was determined by relative position of the nose to the face, extracted from tracker’s bounding boxes. Results show that the method effectively detect head direction indicated by 82% accuracy and very low detection or tracking failure.
关键词：head;detecting;tracking