Audible Obstacle Warning System for Visually Impaired Person Based on Image Processing
Main Article Content
Keywords
Obstacle Warning System, Visually Impaired Person, Haar Like Feature, Hough Transform
Abstract
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.
References
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).
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
The authors declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.