Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker

Fitri Utaminingrum, Yuita Arum Sari, Putra Pandu Adikara, Dahnial Syauqy, Sigit Adinugroho

Abstract


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.


Keywords


head; detecting; tracking

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v16i4.6595

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Universitas Ahmad Dahlan

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


TELKOMNIKA Telecommunication, Computing, Electronics and Control
website: http://telkomnika.ee.uad.ac.id
online system: http://journal.uad.ac.id/index.php/TELKOMNIKA
Phone: +62 (274) 563515, 511830, 379418, 371120 ext: 3208
Fax    : +62 (274) 564604

View TELKOMNIKA Stats