Main Article Content
Obstacle Warning System, Visually Impaired Person, Haar Like Feature, Hough Transform
To be able to do their daily activities, a visually impaired person needs a guidance device to help him/her walk including to avoid obstacles on their way to the destination. The quick and clear instruction is given to the user is the most challenging problem to be solved. The visually impaired person should have simple guidance about the obstruction in front of him/her. Most guidance devices use simple sounds to give the warning without information about which direction the user should go. In this paper, an obstacle warning system based on image processing methods was developed. A guidance device for visually impaired persons using a single-board computer based on an image-processing algorithm has been designed. The main sensor of the guidance device is a NoIR camera. The distance measurement approximation model was developed with errors up to 4.3%. The test found that the proposed system can detect obstruction in the form of a person, the device also detects the stairs. The best detection obtains when the object position is less than 300 cm in front of the user. The stair detection was carried out by using the Hough line transform method. The output of the system is the sound of direction that can be heard through the headset.
S. Hashino and R. Ghurchian, “A blind guidance system for street crossings based on ultrasonic sensors,” in The 2010 IEEE International Conference on Information and Automation, IEEE, Jun. 2010, pp. 476–481. doi: 10.1109/ICINFA.2010.5512383.
A. Sen, K. Sen, and J. Das, “Ultrasonic Blind Stick for Completely Blind People to Avoid Any Kind of Obstacles,” in Proceedings of IEEE Sensors, India: IEEE, Oct. 2018, pp. 1–4. doi: 10.1109/ICSENS.2018.8589680.
S. Shoval, J. Borenstein, and Y. Koren, “Auditory guidance with the navbelt-a computerized travel aid for the blind,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 28, no. 3, pp. 459–467, 1998, doi: 10.1109/5326.704589.
S. Shoval, I. Ulrich, and J. Borenstein, “NavBelt and the GuideCane,” IEEE Robot Autom Mag, vol. 10, no. 1, pp. 9–20, Mar. 2003, doi: 10.1109/MRA.2003.1191706.
M. Tripathi, M. Kumar, V. Kumar, and W. Kandlikar, “Darshan : Electronics Guidance For The Navigation Of Visually Impaired Person,” International Journal For Research in Applied Science and Engineering Technology (IJRASET), vol. 2, no. 6, pp. 335–342, 2014.
A. Abdurrasyid, I. Indrianto, and R. Arianto, “Detection of immovable objects on visually impaired people walking aids,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 17, no. 2, p. 580, Aug. 2018, doi: 10.12928/telkomnika.v17i2.9933.
N. Ma’muriyah, A. Yulianto, and Lili, “Design prototype of audio guidance system for blind by using raspberry pi and fuzzy logic controller,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Sep. 2019, p. 12024. doi: 10.1088/1742-6596/1230/1/012024.
F. Rahmadina and . Z., “Sistem Informasi Lalu Lintas Berbasis Raspberry Pi PC Board,” JURNAL NASIONAL TEKNIK ELEKTRO, vol. 5, no. 1, Mar. 2016, doi: 10.20449/jnte.v5i1.190.
R. Ramiati, S. Aulia, and L. Lifwarda, “Aplikasi Identifikasi Huruf Braille Menggunakan Computer Vision Berbasis Raspberry Pi,” JURNAL NASIONAL TEKNIK ELEKTRO, vol. 9, no. 1, p. 12, Mar. 2020, doi: 10.25077/jnte.v9n1.707.2020.
R. A. Nadafa, S. M. Hatturea, V. M. Bonala, and S. P. Naikb, “Home Security against Human Intrusion using Raspberry Pi,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 1811–1820. doi: 10.1016/j.procs.2020.03.200.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, IEEE Comput. Soc, 2001, pp. I-511-I–518. doi: 10.1109/CVPR.2001.990517.
S. J. Elias et al., “Face recognition attendance system using local binary pattern (LBP),” Bulletin of Electrical Engineering and Informatics, vol. 8, no. 1, pp. 239–245, 2019, doi: 10.11591/eei.v8i1.1439.
G. I. Hapsari, G. A. Mutiara, and H. Tarigan, “Face recognition smart cane using haar-like features and eigenfaces,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 17, no. 2, p. 973, Apr. 2019, doi: 10.12928/telkomnika.v17i2.11772.
Ngo Ba Viet, Nguyen Thanh Hai, and Ngo Van Thuyen, “Hands-free control of an electric wheelchair using face behaviors,” in 2017 International Conference on System Science and Engineering (ICSSE), IEEE, Jul. 2017, pp. 29–33. doi: 10.1109/ICSSE.2017.8030831.
I. Paliy, “Face detection using Haar-like features cascade and convolutional neural network,” in 2008 International Conference on “Modern Problems of Radio Engineering, Telecommunications and Computer Science” (TCSET), 2008, pp. 375–377.
D. Pertsau and A. Uvarov, “Face detection algorithm using haar-like feature for GPU architecture,” in 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), IEEE, Sep. 2013, pp. 726–730. doi: 10.1109/IDAACS.2013.6663020.
Z. Zhu, T. Morimoto, H. Adachi, O. Kiriyama, T. Koide, and H. J. Mattausch, “Multi-view face detection and recognition using haar-like features,” COE Workshop., no. June 2014, pp. 2–4, 2006.
Y. Li, W. Lu, S. Wang, and X. Ding, “Local Haar-like features in edge maps for pedestrian detection,” in 2011 4th International Congress on Image and Signal Processing, IEEE, Oct. 2011, pp. 1424–1427. doi: 10.1109/CISP.2011.6100477.
Zhang Yang, Liu Weiming, Mo Chen, and Li Zilong, “Pedestrian detection in complex scene using full binary tree classifiers based on locally assembled Binary Haar-like features,” in 2011 9th World Congress on Intelligent Control and Automation, IEEE, Jun. 2011, pp. 1180–1184. doi: 10.1109/WCICA.2011.5970702.
L. K. Lee, S. Y. An, and S. Y. Oh, “Efficient face detection and tracking with extended CAMSHIFT and haar-like features,” 2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011, pp. 507–513, 2011, doi: 10.1109/ICMA.2011.5985614.
R. Penrose, “A generalized inverse for matrices,” Mathematical Proceedings of the Cambridge Philosophical Society, vol. 51, no. 3, pp. 406–413, 1955, doi: 10.1017/S0305004100030401.
L. Chandrasekar and G. Durga, “Implementation of Hough Transform for Image Processing Applications,” in International Conference on Communication and Signal Processing, India.
“eSpeak text to speech.” http://espeak.sourceforge.net/ (accessed Aug. 27, 2021).