Detecting Objects Using Haar Cascade for Human Counting Implemented in OpenMV
DOI:
https://doi.org/10.26594/register.v9i2.3175Keywords:
object detection, object counting, computer vision, Haar Cascade Classifier, OpenMV, Human ObjectAbstract
Sight is a fundamental sense for humans, and individuals with visual impairments often rely on assistance from others or tools that promote independence in performing various tasks. One crucial aspect of aiding visually impaired individuals involves the detection and counting of objects. This paper aims to develop a simulation tool designed to assist visually impaired individuals in detecting and counting human objects. The tool's implementation necessitates a synergy of both hardware and software components, with OpenMV serving as a central hardware device in this study. The research software was developed using the Haar Cascade Classifier algorithm. The research process commences with the acquisition of image data through the OpenMV camera. Subsequently, the image data undergoes several stages of processing, including the utilization of the Haar Cascade classifier method within the OpenMV framework. The resulting output consists of bounding boxes delineating the detection areas and the tally of identified human objects. The results of human object detection and counting using OpenMV exhibit an accuracy rate of 71%. Moreover, when applied to video footage, the OpenMV system yields a correct detection rate of 73% for counting human objects. In summary, this study presents a valuable tool that aids visually impaired individuals in the detection and counting of human objects, achieving commendable accuracy rates through the implementation of OpenMV and the Haar Cascade Classifier algorithm.
References
V. Kunta, C. Tuniki, and U. Sairam, “Multi-Functional Blind Stick for Visually Impaired People,” in 2020 5th International Conference on Communication and Electronics Systems (ICCES), IEEE, Jun. 2020, pp. 895–899. doi: 10.1109/ICCES48766.2020.9137870.
S. Zaib, S. Khusro, S. Ali, and F. Alam, “Smartphone Based Indoor Navigation for Blind Persons using User Profile and Simplified Building Information Model,” in 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), IEEE, Jul. 2019, pp. 1–6. doi: 10.1109/ICECCE47252.2019.8940799.
S. Kumar KN, R. Sathish, S. Vinayak, and T. Parasad Pandit, “Braille Assistance System for Visually Impaired, Blind & Deaf-Mute people in Indoor & Outdoor Application,” in 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), IEEE, May 2019, pp. 1505–1509. doi: 10.1109/RTEICT46194.2019.9016765.
J. Zhu et al., “An Edge Computing Platform of Guide-dog Robot for Visually Impaired,” in 2019 IEEE 14th International Symposium on Autonomous Decentralized System (ISADS), IEEE, Apr. 2019, pp. 1–7. doi: 10.1109/ISADS45777.2019.9155620.
J. Bai, S. Lian, Z. Liu, K. Wang, and D. Liu, “Virtual-Blind-Road Following-Based Wearable Navigation Device for Blind People,” IEEE Transactions on Consumer Electronics, vol. 64, no. 1, pp. 136–143, Feb. 2018, doi: 10.1109/TCE.2018.2812498.
T. Stahl, S. L. Pintea, and J. C. van Gemert, “Divide and Count: Generic Object Counting by Image Divisions,” IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 1035–1044, Feb. 2019, doi: 10.1109/TIP.2018.2875353.
G. Gao, Q. Liu, and Y. Wang, “Counting From Sky: A Large-Scale Data Set for Remote Sensing Object Counting and a Benchmark Method,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 3642–3655, May 2021, doi: 10.1109/TGRS.2020.3020555.
H. Jha, V. Lodhi, and D. Chakravarty, “Object Detection and Identification Using Vision and Radar Data Fusion System for Ground-Based Navigation,” in 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, Mar. 2019, pp. 590–593. doi: 10.1109/SPIN.2019.8711717.
W. Raza, A. Osman, F. Ferrini, and F. De Natale, “Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs,” Drones, vol. 5, no. 4, p. 127, Oct. 2021, doi: 10.3390/drones5040127.
I. Abdelkader, Y. El-Sonbaty, and M. El-Habrouk, “OPENMV: A PYTHON POWERED, EXTENSIBLE MACHINE VISION CAMERA,” 2017.
P. Sridhar, P. Chithaluru, S. Kumar, O. Cheikhrouhou, and H. Hamam, “An Enhanced Haar Cascade Face Detection Schema for Gender Recognition,” in 2023 International Conference on Smart Computing and Application (ICSCA), IEEE, Feb. 2023, pp. 1–5. doi: 10.1109/ICSCA57840.2023.10087742.
R. Y. Adhitya, A. Khumaidi, S. T. Sarena, S. Kautsar, B. Widiawan, and F. L. Afriansyah, “Applied Haar Cascade and Convolution Neural Network for Detecting Defects in The PCB Pathway,” in 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), IEEE, Nov. 2020, pp. 408–411. doi: 10.1109/CENIM51130.2020.9297996.
M. P. Anggadhita and Y. Widiastiwi, “Breaches Detection in Zebra Cross Traffic Light Using Haar Cascade Classifier,” in 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), IEEE, Nov. 2020, pp. 272–277. doi: 10.1109/ICIMCIS51567.2020.9354275.
A. Raghunandan, Mohana, P. Raghav, and H. V. R. Aradhya, “Object Detection Algorithms for Video Surveillance Applications,” in 2018 International Conference on Communication and Signal Processing (ICCSP), IEEE, Apr. 2018, pp. 0563–0568. doi: 10.1109/ICCSP.2018.8524461.
L. Zhao and Y. Wan, “A New Deep Learning Architecture for Person Detection,” in 2019 IEEE 5th International Conference on Computer and Communications (ICCC), IEEE, Dec. 2019, pp. 2118–2122. doi: 10.1109/ICCC47050.2019.9064172.
S. Zhang, H. Li, W. Kong, L. Wang, and X. Niu, “An object counting network based on hierarchical context and feature fusion,” J Vis Commun Image Represent, vol. 62, pp. 166–173, Jul. 2019, doi: 10.1016/j.jvcir.2019.05.003.
A. Börold, M. Teucke, A. Rust, and M. Freitag, “Deep Learning-based Object Recognition for Counting Car Components to Support Handling and Packing Processes in Automotive Supply Chains,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 10645–10650, 2020, doi: 10.1016/j.ifacol.2020.12.2828.
S. Kakehi et al., “Identification and counting of Pacific oyster Crassostrea gigas larvae by object detection using deep learning,” Aquac Eng, vol. 95, p. 102197, Nov. 2021, doi: 10.1016/j.aquaeng.2021.102197.
Z. Liu, Q. Wang, and F. Meng, “A benchmark for multi-class object counting and size estimation using deep convolutional neural networks,” Eng Appl Artif Intell, vol. 116, p. 105449, Nov. 2022, doi: 10.1016/j.engappai.2022.105449.
H. Yang et al., “Multi-object tracking using Deep SORT and modified CenterNet in cotton seedling counting,” Comput Electron Agric, vol. 202, p. 107339, Nov. 2022, doi: 10.1016/j.compag.2022.107339.
J. Rodriguez-Vazquez, A. Alvarez-Fernandez, M. Molina, and P. Campoy, “Zenithal isotropic object counting by localization using adversarial training,” Neural Networks, vol. 145, pp. 155–163, Jan. 2022, doi: 10.1016/j.neunet.2021.10.010.
H. Li, W. Kong, and S. Zhang, “Deeply scale aggregation network for object counting,” Knowl Based Syst, vol. 210, p. 106485, Dec. 2020, doi: 10.1016/j.knosys.2020.106485.
Y. Zhang, W. Zhang, J. Yu, L. He, J. Chen, and Y. He, “Complete and accurate holly fruits counting using YOLOX object detection,” Comput Electron Agric, vol. 198, p. 107062, Jul. 2022, doi: 10.1016/j.compag.2022.107062.
A. AbdelRaouf and D. Salama, “Handwritten Signature Verification using Haar Cascade Classifier Approach,” in 2018 13th International Conference on Computer Engineering and Systems (ICCES), IEEE, Dec. 2018, pp. 319–326. doi: 10.1109/ICCES.2018.8639437.
S. Dawn, S. Tulsyan, S. Bhattarai, S. Gopal, and V. Saxena, “An Efficient Approach to Image Indexing and Retrieval Using Haar Cascade and Perceptual Similarity Index,” in 2020 6th International Conference on Signal Processing and Communication (ICSC), IEEE, Mar. 2020, pp. 108–113. doi: 10.1109/ICSC48311.2020.9182741.
A. V. and H. G. B., “Prototype Design of Intelligent Traffic Signal Control using Haar Cascade Classifier,” in 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), IEEE, Mar. 2021, pp. 260–264. doi: 10.1109/WiSPNET51692.2021.9419366.
A. Priadana and M. Habibi, “Face Detection using Haar Cascades to Filter Selfie Face Image on Instagram,” in 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), IEEE, Mar. 2019, pp. 6–9. doi: 10.1109/ICAIIT.2019.8834526.
D. K. Ulfa and D. H. Widyantoro, “Implementation of haar cascade classifier for motorcycle detection,” in 2017 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), IEEE, Nov. 2017, pp. 39–44. doi: 10.1109/CYBERNETICSCOM.2017.8311712.
T. Mantoro, M. A. Ayu, and Suhendi, “Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods,” in 2018 6th International Conference on Multimedia Computing and Systems (ICMCS), IEEE, May 2018, pp. 1–5. doi: 10.1109/ICMCS.2018.8525935.
I. Plauska, A. Liutkevi?ius, and A. Janavi?i?t?, “Performance Evaluation of C/C++, MicroPython, Rust and TinyGo Programming Languages on ESP32 Microcontroller,” Electronics (Basel), vol. 12, no. 1, p. 143, Dec. 2022, doi: 10.3390/electronics12010143.
M. El-Habrouk, I. Abdelkader, and Y. El-Sonbaty, “Openmv: A Python powered, extensible machine vision camera,” 2017. [Online]. Available: https://www.researchgate.net/publication/321347754
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Mustika Mentari, Rosa Andrie Asmara, Kohei Arai, Haidar Sakti Oktafiansyah
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Please find the rights and licenses in Register: Jurnal Ilmiah Teknologi Sistem Informasi. By submitting the article/manuscript of the article, the author(s) agree with this policy. No specific document sign-off is required.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author(s)' Warranties
The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
3. User/Public Rights
Register's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, Register permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and Register on distributing works in the journal and other media of publications. Unless otherwise stated, the authors are public entities as soon as their articles got published.
4. Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
Copyright and other proprietary rights relating to the article, such as patent rights,
The right to use the substance of the article in own future works, including lectures and books,
The right to reproduce the article for own purposes,
The right to self-archive the article (please read out deposit policy),
The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Register: Jurnal Ilmiah Teknologi Sistem Informasi).
5. Co-Authorship
If the article was jointly prepared by more than one author, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. Register will not be held liable for anything that may arise due to the author(s) internal dispute. Register will only communicate with the corresponding author.
6. Royalties
Being an open accessed journal and disseminating articles for free under the Creative Commons license term mentioned, author(s) aware that Register entitles the author(s) to no royalties or other fees.
7. Miscellaneous
Register will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. Register's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.