View of CLASSIFICATION OF GUIDING BLOCKS FOR THE VISUALLY IMPAIRED USING HOG AND SVM

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CLASSIFICATION OF GUIDING BLOCKS FOR THE VISUALLY IMPAIRED USING HOG AND SVM

Nurul Fathanah Mustamin^1)*, Muhammad Afief Ma’Ruf ²⁾, dan Rafif Taufiqurrahman ³⁾

1, 2,3) Universitas Lambung Mangkurat

Jl. Brigjen H. Hasan Basri, Pangeran, Kec. Banjarmasin Utara, Kota Banjarmasin, Kalimantan Selatan 70123 e-mail: [email protected]¹⁾, [email protected]²⁾, [email protected]³⁾

*e-mail korespondensi : [email protected] ABSTRAK

Semua warga negara, termasuk mereka yang memiliki keterbatasan fisik, harus dapat menggunakan fasilitas dan layanan publik tanpa masalah. Prinsip aksesibilitas—kenyamanan, keamanan, kegunaan, dan kemandirian—harus diikuti jika kita ingin mencapai kesejahteraan sosial di setiap bidang kehidupan. Bagi mereka yang memiliki keterbatasan gerak atau penglihatan, blok pemandu (juga dikenal sebagai ubin pemandu atau ubin peringatan) dapat berfungsi sebagai indikasi pent- ing. Sayangnya, tidak semua orang tunanetra menggunakan fungsi ini dalam penyiapannya saat ini. Gunakan Support Vector Machine (SVM) dan His-togram of Oriented Gradient (HOG) untuk mengekstraksi fitur untuk digunakan dalam kategorisasi gambar blok Bimbingan. Pada ambang 75, akurasi klasifikasi blok pemandu adalah 99,51 persen dalam jarak 30 cm, 74,811 persen dalam jarak 50 cm, dan 82,221 persen dalam jarak 80 cm.

Kata Kunci: Guiding Block, Computing Methodologies, HOG, Machine learning, Disability, Image Processing, Image Clas- sification, Blind, SVM.

ABSTRACT

All citizens, including those with physical limitations, should be able to use public facilities and services without issue. The principles of accessibility—convenience, safety, usability, and independence—must be followed if we are to achieve social welfare in every area of life. For those with mobility or sight impairments, a guiding block (also known as a guiding tile or warning tile) can serve as an important indication. Regrettably, not all visually impaired people make use of this function in its current setup. Use a Support Vector Machine (SVM) and a His-togram of Oriented Gradient (HOG) to extract features for use in Guidance block image categorization. At a threshold of 75, the guiding blocks' classification accuracy is 99.51 percent within 30 cm, 74.811 percent within 50 cm, and 82.221 percent within 80 cm.

Keywords: Guiding Block, Computing Methodologies, HOG, Machine learning, Disability, Image Processing, Image Classi- fica-tion, Blind, SVM.

I. INTRODUCTION

s part of its city planning, the growing metropolis of B ANJARMASIN has begun enhancing its pedestrian walkways and other public amenities. As part of the 2018 Mayor's plan to make "BAIMAN (Barasih wan Nyaman)" Banjarmasin City a reality, the cityscape rehabilitation project for 2018-2019 has been completed. The purpose of this layout is to make the sidewalk more accessible for pedestrians, especially those with mobility issues. Banjarmasin is home to 3,897 disabled individuals (65.1% are physically challenged, 34.3%

are mentally retarded) in 5 subdistricts, 52 urban villages, 117 RW, and 1,857 RT [1]. The Guidance Building is one of several new buildings being built to accommodate people with special needs.

A guiding block, which may also be referred to as a guiding tile or a warning tile, is a type of sign developed exclusively for persons with impairments, particularly those who are blind. This will comply with the Technical Guidelines for Facilities and Accessibility in Buildings and Environment Regulation No. 30, issued by the Minister of Public Works in 2006 and published in the Official Gazette of Canada. The term "public buildings and the environment" as used in this ministerial order encompasses both privately owned and publicly owned structures, as well as their surrounding environments. Private homes owned by individuals that are open to the general public and used by everyone, including individuals with impairments [2][3]. The pursuit of accessibility should be guided by its guiding principles: independence, convenience, safety, and ease of use. These are the four pillars of accessibility. Unfortunately, because of the way it is now configured, not all blind people make use of this capability.

Blind people have limited sensory experience and must rely on the experiences of others in their day-to-day lives, which makes it challenging for them to conduct activities [4][5]. The system uses an image processing algorithm to determine whether the guiding block image process is leading to a straight line or a stop, or whether there are obstacles in the range. It is hoped that this technique, when combined with a classification method and a white

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research on guiding blocks has been carried out by several researchers [6][7][8] in the past.

The HOG and SVM classification methods have been employed in some investigations, including the research on individuals conducted by Nithyakani and Ferni. The importance of visual monitoring devices is shown by gait, a behavioral parameter assessed at a distance. The HOG and SVM were put through their paces on the 19,139- image CASIA A database. The results were 87% accurate when recognizing people in low-resolution pictures [9].

Using a method that transforms images into a computer-editable format, this research evaluates the efficacy of Neural Networks and Support Vector Machines compared to the HOG feature extraction method [10]. Rahmad et al. compared the Viola-Jones Haar Cascade Classifier and the HOG methods for recognizing faces. The HOG method had the highest accuracy after five trials, according to the study's findings, with an accuracy of 80.22%.[11].

These researches suggest that the HOG and SVM methods can be utilized to develop a prototype or design for a system that can classify guiding blocks. The following application, designed to help the visually handicapped navigate their environments by identifying and navigating directional blocks, stands to benefit greatly from the use of this technology.

II. RESEARCH METODOLOGY

A. Data Collection

The camera is placed at a distance of 30 centimeters from the guiding block, and a total of 450 photographs are used in this study, 150 of which feature the block with a bold design, 150 feature the block with a stop pattern, and 150 feature a typical pedestrian street. To avoid making incorrect classifications, researchers need 75–100 data points. Data collection consisted of making in-person observations at the study site on A. Yani Street near the 3rd kilometer marker on the way to Banjarmasin. An example of the used image data is shown in Figure 1.

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Figure 1. Guiding Block with (a) Forward Pattern Sign, (b) Stop Pattern Sign, and (c) Normal Pedestrian Street

B. Preparing the Data

The collected image data will have its RGB values converted to grayscale during this procedure stage. During the preprocessing phase of an image's creation, any background interference should be eliminated, and the impact of brightness should be toned down. This method uses the OpenCV package and the cv from the standard Python library. Colour Function BGR2GRAY Conversion.

C. Segmentation Data

In picture segmentation, thresholding is used to separate foreground from background elements. The cv.threshold function and the OpenCV module from Python's standard library see heavy use.

D. Image Detection with HOG

To find the object, the HOG approach first applied thresholding to the image to segment it, and then the image was divided into many cells and organized into blocks so that the cells intersected with each other. During this process, we use an auxiliary library, specifically the skiimage library. After HOG extraction, the sums of each image are calculated, and the results are imported into Excel with the help of pandas library functions..

E. SVM-Based Image Classification

The outcomes of applying the HOG method to the image data were copied and pasted into a spreadsheet created in Microsoft Excel. The last step involves applying the SVM algorithm to classify the photos of the guiding blocks.

The combination of the proportion of data will be separated into 70:30 for this classification process, with 70% of the data serving as training data and 30% as testing data.

F. System Performance Evaluation

The confusion Matrix is used to analyze the results of the HOG and SVM classifications performed on the Guid- ance Block picture. The Confusion Matrix is a mechanism that can be implemented to carry out a performance testing for various categorization strategies. The accuracy, precision, and recall numbers that Confusion Matrix produces are determined by the dataset that is being evaluated [12][13]. To assess the performance of the model,

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3 the F1-Score is also utilized. The F1-Score indicates how well the player balances recall and precision. Accuracy and the F1-Score both serve the same purpose. On the other hand, the F1-Score might be helpful in cases with an uneven distribution of classes (the number of False Negatives is higher). The equations (1), (2), (3), and (4) indicate the various formulas that are used to determine accuracy, precision, and recall, as well as the F1-Score.

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴= 𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇+𝐹𝐹𝑇𝑇+𝐹𝐹𝑇𝑇^{𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇} (1)

𝑃𝑃𝐴𝐴𝑃𝑃𝐴𝐴𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃= _{𝑇𝑇𝑇𝑇+𝐹𝐹𝑇𝑇}^{𝑇𝑇𝑇𝑇} (2)

𝑅𝑅𝑃𝑃𝐴𝐴𝐴𝐴𝑅𝑅𝑅𝑅= _{𝑇𝑇𝑇𝑇+𝐹𝐹𝑇𝑇}^{𝑇𝑇𝑇𝑇} (3)

𝐹𝐹1− 𝑆𝑆𝐴𝐴𝑃𝑃𝐴𝐴𝑃𝑃= 2 𝑥𝑥 𝑇𝑇𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑥𝑥 𝑅𝑅𝑃𝑃𝑃𝑃𝑅𝑅𝑅𝑅𝑅𝑅

𝑇𝑇𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃+𝑅𝑅𝑃𝑃𝑃𝑃𝑅𝑅𝑅𝑅𝑅𝑅 (4)

III. RESULTANDDISCUSSION

At distances of 30, 50, and 80 centimeters, the outcomes of the system were analyzed and evaluated twice for each data point. Data were collected for Accuracy, Precision, Recall, and F1-score during the first test, which was carried out without threshold segmentation. The subsequent test will involve adjusting the threshold for picture segmentation to values ranging from 50 to 75, 100 to 110, and 120, respectively.

A. Experiment results from a 30-centimeter-long distance

For a threshold value of 75, the results showed an Accuracy of 98.51%, Precision of 98.51%, Recall of 98.51%, and F1-score of 98.51%; the remaining results with varying threshold values are provided in Table 1.

TABLE 1.30 CM DATA VALIDATION TEST RESULTS

Threshold Accuracy Precision Recall F1-Score

50 85,82% 88,435% 85,82% 85,55%

75 98,51% 98,51% 98,58% 98,51%

100 93,28% 93,72% 93,28% 93,34%

110 70.85% 82,94% 79,85% 80,21%

125 61,94% 61,95% 61,95% 61,94%

B. A. Experiment results from a 50-centimeter-long distance

With a threshold value of 75, the test results showed an Accuracy of 74.81%, Precision of 74.81%, Recall of 74.81%, and F1-score of 74.81%, respectively, with the rest of the findings for different threshold values presented in Table 2.

50 72.592% 72.592% 75.151% 71.035%

75 74.814% 74.814% 74.707% 74.243%

100 71.111% 71.111% 76.456% 71.064%

110 68.888% 68.888% 69.498% 66.902%

125 64.445% 64.445% 66.387% 64.005%

C. A. Experiment results from a 80-centimeter-long distance

Test outcomes showed an accuracy of 82.22%, precision of 82.22%, recall of 83.14%, and F1-score of 82.01%.

Table 3 displays the outcomes for the 75-point threshold and subsequent thresholds.

50 77.778% 77.778% 78.932% 77.736%

75 82.222% 82.222% 83.145% 82.007%

100 78.789% 78.789% 79.147% 78.770%

110 74.445% 74.445% 75.393% 74.466%

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They used data from experiments with subject distances of 30 cm, 50 cm, and 80 cm. The results reveal that at a threshold value of 75, accuracy is 98.51% within 30 cm, 74.81% within 50 cm, and 82.22 percent within 80 cm.

Table 4 summarizes the data for the 75-point threshold.

TABLE 4.TESTRESULTSSUMMARYATTHETHRESHOLDOF75 Distance Data Obtain

(cm)

Accuracy (%)

Precision (%) Recall (%) F1-Score (%)

30 98.51 98.51 98.58 98.51

50 74.81 74.14 74.71 74.24

80 82.22 82.22 83.15 82.01

This test's results depend on some factors:

a. Incorrect identifications may occur if the database contains textures or patterns with unusual or ambiguous characteristics; data from guiding block pictures can be classified if the texture or pattern makes up at least 70% of the picture.

b. Forward-pattern guiding blocks have a pattern or texture of four straight lines, while stop-pattern guiding blocks have a texture of a circle. Thus, object purity is required for reader-guiding block picture data classification.

c. The photography process significantly impacts the final image of the guiding block. Shadows behind the subject grow or shrink depending on the light's intensity, the position of the camera, and the subject's proximity to the lens. Also, it is effective. So, when recognizing reader block image data, it is important to take into account a wide range of picture stability and consistency factors.

d. Directional blocks with either forward patterns/textures or stop patterns/textures were used in this investi- gation. d. These two features are not included in the shortened conditions, color, or size criteria for directing blocks.

IV. CONCLUSION

The results of the study on the use of the HOG and SVM algorithms in the categorization of directional blocks are as follows:

a. An application for classification was built using Python and its various libraries, with the Histogram of the Oriented Gradient method as the backbone and a Support Vector Machine for pattern recognition.

b. The Histogram of Oriented Gradient extraction method and classification using the Support Vector Machine method achieved a 98.51 percent accuracy rate for a 30 cm distance, 74.811 percent for a 50 cm distance, and 82.22 percent accuracy rate for an 80 cm distance at a threshold value of 75.

c. .

REFERENCE

[1] UNESCO, “Kota Banjarmasin: Sebuah Profil Kota Ramah Disabilitas.” Jakarta, 2019.

[2] Menteri Pekerjaan Umum, “Peraturan Menteri Pekerjaan Umum Nomor : 30/Prt/M/2006 Tentang Pedoman Teknis Fasilitas dan Aksesibilitas Pada Bangunan Gedung dan Lingkungan,” vol. 66. Jakartab, pp. 37–39, 2006.

[3] Pemerintah Republik Indonesia (RI), “Undang-Undang Republik Indonesia Nomor 8 Tahun 2016 Tentang Penyan- dang Disabilitas.” Jakarta, p. 2016, 2016.

[4] A. Azzahro and D. Kurniadi, “Penggunaan Tongkat pada Siswa Tunanetra SMALB dalam Melakukan Mobilitas,”

JASSI_anakku, vol. 18, no. 1, pp. 19–25, 2017, doi: https://doi.org/10.17509/jassi.v17i1.7653.

[5] P. S. Kahayu, R. T. Utami, G. Vernanda, P. Pendidikan, and L. Biasa, “Efektifitas Metode Demonstrasi dalam Ke- mampuan Penggunaan Tongkat Bagi Anak Tunanetra Di SLB A Bina Insani Kota Bandar Lampung,” SNEED: Jurnal Pendidikan Khusus, vol. 2, no. 2, pp. 001–009, 2022.

[6] D. R. Zulfikar, “Trotoar dan Guide Block untuk Penyandang Tunanetra dengan Implementasi Image Processing Menggunakan Metode CNN dan Tensorflow,” Universitas Negeri Malang, 2021. [Online]. Available: http://reposi- tory.um.ac.id/151853/

[7] T. Bonita and A. L. Lukman, “Accessibility Evaluation of The Transit Plaza of Tanah Abang Station According To Users With Visual Impairment,” Jurnal RISA (Riset Arsitektur), vol. 6, no. 1, pp. 1–19, 2022, [Online]. Available:

www.journal.unpar.ac.id

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5 [8] R. Ramadhana, “Rancang Bangun Sarung Tangan Sebagai Alat Bantu Tuna Netra Berbasis Sensor Ultrasonic Dan Arduino NANO,” Jurnal Elektro dan Telekomunikasi Terapan, vol. 7, no. 2, p. 877, Mar. 2021, doi:

10.25124/jett.v7i2.3422.

[9] P. Nithyakani and M. Ferni Ukrit, “Person Identification Using Histogram of Gradient and Support Vector Machine on GEI,” pp. 471–478, 2021, doi: 10.1007/978-981-15-8530-2_38.

[10] M. R. Phangtriastu, J. Harefa, and D. F. Tanoto, “Comparison between Neural Network and Support Vector Machine in Optical Character Recognition.” Jakarta, 2017.

[11] C. Rahmad, R. A. Asmara, D. R. H. Putra, I. Dharma, H. Darmono, and I. Muhiqqin, “Comparison of Viola-Jones Haar Cascade Classifier and Histogram of Oriented Gradients (HOG) for Face Detection,” IOP Conf Ser Mater Sci Eng, vol. 732, no. 1, Jan. 2020, doi: 10.1088/1757-899X/732/1/012038.

[12] I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, “Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem,” Technologies (Basel), vol. 9, no. 4, p. 81, Nov. 2021, doi: 10.3390/technologies9040081.

[13] D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sen- timen Berbasis Teks Pada Twitter,” Jurnal Sains Komputer & Informatika (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.

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