Detection and Classification of Signage’s from Random Mobile Videos Using Local Binary Patterns

Gornale, Shivanand S.; Babaleshwar, Ashvini K; Yannawar, Pravin

doi:10.5815/ijigsp.2018.02.06

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…The steps occur here frame by frame. [21]. The frame preprocessing is done in four steps, which are resizing, histogram equalization, Laplacian of Gaussian and Binarization respectively.…”

Section: Detection and Recognition Of Traffic Sign From Video Inputmentioning

confidence: 99%

Traffic Sign Detection and Recognition Model Using Support Vector Machine and Histogram of Oriented Gradient

Ahmed¹,

Rabbi²,

Rahman³

et al. 2021

IJITCS

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Traffic signs are symbols erected on the sides of roads that convey the road instructions to its users. These signs are essential in conveying the instructions related to the movement of traffic in the streets. Automation of driving is essential for efficient navigation free of human errors, which could otherwise lead to accidents and disorganized movement of vehicles in the streets. Traffic sign detection systems provide an important contribution to automation of driving, by helping in efficient navigation through relaying traffic sign instructions to the system users. However, most of the existing techniques have proposed approaches that are mostly capable of detection through static images only. Moreover, to the best of the author’s knowledge, there exists no approach that uses video frames. Therefore, this article proposes a unique automated approach for detection and recognition of Bangladeshi traffic signs from the video frames using Support Vector Machine and Histogram of Oriented Gradient. This system would be immensely useful in the implementation of automated driving systems in Bangladeshi streets. By detecting and recognizing the traffic signs in the streets, the automated driving systems in Bangladesh will be able to effectively navigate the streets. This approach classifies the Bangladeshi traffic signs using Support Vector Machine classifier on the basis of Histogram of Oriented Gradient property. Through image processing techniques such as binarization, contour detection and identifying similarity to circle etc., this article also proposes the actual detection mechanism of traffic signs from the video frames. The proposed approach detects and recognizes traffic signs with 100% precision, 95.83% recall and 96.15% accuracy after running it on 78 Bangladeshi traffic sign videos, which comprise 6 different kinds of Bangladeshi traffic signs. In addition, a public dataset for Bangladeshi traffic signs has been created that can be used for other research purposes.

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“…The steps occur here frame by frame. [21]. The frame preprocessing is done in four steps, which are resizing, histogram equalization, Laplacian of Gaussian and Binarization respectively.…”

Section: Detection and Recognition Of Traffic Sign From Video Inputmentioning

confidence: 99%

Traffic Sign Detection and Recognition Model Using Support Vector Machine and Histogram of Oriented Gradient

Ahmed¹,

Rabbi²,

Rahman³

et al. 2021

IJITCS

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“…Sistem ini menggunakan metode Local Binary Pattern (LBP) untuk transformasi sebagai tahap awal ekstraksi ciri dan menggunakan metode ektraksi ciri statistik. LBP adalah metode analisis tekstur yang menggunakan model statistika dan struktur [4]- [6]. LBP bekerja dengan melakukan pemberian label pixel pada suatu citra berdasarkan thresholding ketetanggaan dari setiap pixel dan merepresentasikannya dalam bentuk biner [7].…”

Section: Pendahuluanunclassified

Kombinasi Metode Logical Binary Pattern dan K-Nearest Neighbor untuk Identifikasi Lubang pada Jalan Aspal

Arhandi

Mentari

Romadhon

2021

j. nas. pendidik. teknik. inform.

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Kondisi jalan yang baik akan memudahkan segala aktivitas masyarakat. Terjadinya kerusakan pada jalan akan berakibat bukan hanya terhalangnya kegiatan ekonomi dan sosial namun akan berpengaruh pada keamanan dan kenyamanan pemakai jalan. Salah satu kendala utama yang menyebabkan lamanya perbaikan kerusakan jalan yaitu proses pendeteksian dan pencatatan kerusakan. Proses tersebut saat ini dilakukan secara manual dengan bantuan tenaga manusia. Karena proses pendeteksian dan pendataan masih dilakukan secara manual dengan tenaga manusia, waktu yang dibutuhkan relatif lama dan tingkat keakuratannya rendah. Oleh karena itu, pada penelitian ini dikembangkan sebuah sistem yang dapat menjadi alternatif Dinas Pembangunan Umum untuk mengidentifikasi lubang pada aspal sehingga dapat mempercepat proses perbaikan jalan. Sistem yang dikembangkan berhasil mengidentifikasi lubang pada jalan aspal menggunakan kombinasi metode Local Binary Pattern (LBP) dan K-Nearest Neighbor (KNN).Local Binary Pattern (LBP) untuk transformasi sebagai tahap awal ekstraksi ciri. Metode K- Nearest Neighbor (KNN) digunakan untuk klasifikasi citra jalan aspal berlubang atau tidak.Proses pengujian pada penelitian ini terdiri dari dua pengujian utama, yaitu pengujian unit dan pengujian akurasi. Pengujian unit dilakukan dengan black box testing. Pada pengujian akurasi diperoleh akurasi sebesar 96% dengan FAR sebesar 0% dan FRR sebesar 4,16%. Pada proses perhitungan lubang pada aspal diperoleh akurasi yang sangat baik yaitu sebesar 97,74%.

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“…K-Nearest Neighbor classifier: will classify the class label based on measuring the distance between testing and training data. KNN [46,48,49] will classify by suitable K value which in turn finds the nearest neighbor and provides a class label to un-labeled images. Depending on the types of problem, a variety of different distance measures can be implemented.…”

Section: Classifiersmentioning

confidence: 99%

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

Patil¹,

Kruthi²,

Gornale³

2019

IJIGSP

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A certain number of researchers have utilized uni-modal bio-metric traits for gender classification. It has many limitations which can be mitigated with inclusion of multiple sources of biometric information to identify or classify user's information. Intuitively multimodal systems are more reliable and viable solution as multiple independent characteristics of modalities are fused together. The objective of this work is inferring the gender by combining different biometric traits like face, iris, and fingerprints of same subject. In the proposed work, feature level fusion is considered to obtain robustness in gender determination; and an accuracy of 99.8% was achieved on homologous multimodal biometric database SDUMLA-HMT (Group of Machine Learning and Applications, Shandong University). The results demonstrate that the feature level fusion of Multimodal Biometric system greatly improves the performance of gender classification and our approach outperforms the state-of-the-art techniques noticed in the literature.

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Detection and Classification of Signage’s from Random Mobile Videos Using Local Binary Patterns

Cited by 7 publications

References 9 publications

Traffic Sign Detection and Recognition Model Using Support Vector Machine and Histogram of Oriented Gradient

Traffic Sign Detection and Recognition Model Using Support Vector Machine and Histogram of Oriented Gradient

Kombinasi Metode Logical Binary Pattern dan K-Nearest Neighbor untuk Identifikasi Lubang pada Jalan Aspal

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

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