Vehicle detection using background subtraction and clustering algorithms

Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising

Application of neural network method for road crack detection

The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%

Penerapan Metode K-Means Berbasis Jarak untuk Deteksi Kendaraan Bergerak

Deteksi kendaraan bergerak adalah salah satu elemen penting dalam aplikasi Intelligent Transport System (ITS). Deteksi kendaraan bergerak juga merupakan bagian dari pendeteksian benda bergerak. Metode K-Means berhasil diterapkan pada piksel cluster yang tidak diawasi untuk mendeteksi objek bergerak. Secara umum, K-Means adalah algoritma heuristik yang mempartisi kumpulan data menjadi K cluster dengan meminimalkan jumlah kuadrat jarak di setiap cluster. Dalam makalah ini, algoritma K-Means menerapkan jarak Euclidean, jarak Manhattan, jarak Canberra, jarak Chebyshev dan jarak Braycurtis. Penelitian ini bertujuan untuk membandingkan dan mengevaluasi implementasi jarak tersebut pada algoritma clustering K-Means. Perbandingan dilakukan dengan basis K-Means yang dinilai dengan berbagai parameter evaluasi yaitu MSE, PSNR, SSIM dan PCQI. Hasilnya menunjukkan bahwa jarak Manhattan memberikan nilai MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 dan PCQI = 0.79 terbaik dibandingkan dengan jarak lainnya. Sedangkan untuk waktu pemrosesan data memperlihatkan bahwa jarak Braycurtis memiliki keunggulan lebih yaitu 0.3 detik. AbstractDetection moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detection moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 and PCQI = 0.79 values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages

PENERAPAN ACTIVE CONTOUR MODEL PADA PENGOLAHAN CITRA UNTUK DETEKSI KERUSAKAN JALAN

Road damage is a serious problem because it often occurs everywhere. Damage to the road surface, such as potholes, often disrupts land transportation, and can even cause accidents. With the automatic detection of road damage types, it can simplify the process of classifying the types of road damage by using images from the results of the classification system which can be used as supporting information in calculating road repairs. In this study, to identify road damage types by images, the active contour model segmentation technique is used based on the level set and then classified by the support vector machine method. Based on the test results, using 58 data sets with 12 types of road damage, the accuracy of this method is 87.93%.Kerusakan pada permukaan jalan seperti jalan berlubang sering kali mengganggu pada transportasi darat, bahkan dapat menyebabkan kecelakaan. Dengan adanya deteksi jenis kerusakan jalan secara otomatis, dapat mempermudah proses klasifikasi jenis kerusakan jalan dengan menggunakan citra dari hasil klasifikasi sistem yang selanjutnya dapat digunakan sebagai informasi pendukung pada perhitungan perbaikan jalan. Pada penelitian ini untuk identifikasi citra kerusakan jalan digunakan teknik segmentasi active contour model berbasis level set kemudian diklasifikasikan dengan metode support vector machine. Berdasarkan hasil pengujian, dengan menggunakan 58 data set dengan 12 jenis kerusakan jalan, didapatkan hasil akurasi dari metode ini adalah sebesar 87,93%

Perbandingan Metode Pembobotan Tf-Rf Dan Tf-Idf Dikombinasikan Dengan Weighted Tree Similarity Untuk Sistem Rekomendasi Buku

Unit Pusat Terpadu Perpustakaan merupakan perpustakaan pusat yang ada di Universitas Lambung Mangkurat. Perpustakaan ini mempunyai sistem pencarian buku namun sistem tersebut belum adanya fitur rekomendasi buku sehingga anggota menjadi kesulitan dalam melakukan pencarian buku yang sesuai dengan keinginan anggota. Oleh karena itu, dengan adanya rekomendasi buku atau saran buku yang lain dapat menjadi alternatif untuk membantu anggota dalam melakukan pencarian buku yang sesuai. Dalam penelitian ini menggunakan perbandingan pembobotan kata TF-IDF dan TF-RF dengan weighted tree similarity sebagai pengukur kemiripan diantara beberapa data dengan parameter tree yang sudah ditentukan dan dilakukan perbandingan perhitungan dengan menghitung tf-idf dengan tf-rf menggunakan perhitungan excel mendapatkan nilai yang berbeda antara tf-idf dengan tf-rf, pembobotan tf-idf dapat mengukur kemiripan antara dokumen dan kata kunci buku yang paling mirip dengan buku yang dianggap paling relevan. Sehingga anggota memasukan kata kunci kemudian akan menemukan kemiripan buku dari kata kunci yang dimasukan sebelumnya namun untuk pembobotan tf-rf memberikan kata kunci dari setiap kategori. Hasil perbandingan yang di dapat yaitu 96% untuk tf-idf dan 98% untuk tf-rf. Sistem ini menggunakan bahasa pemrograman python dengan web framework django. AbstractThe Central Integrated Library Unit is the central library at Lambung Mangkurat University. This library has a book search system but the system does not have a book recommendation feature so that members find it difficult to search for books that match the wishes of members. Therefore, the existence of book recommendations or other book suggestions can be an alternative to assist members in searching for suiTabel books. In this study using a comparison of the weighting of the words TF-IDF and TF-RF with weighted tree similarity as a measure of the similarity between several data and a comparison of calculations is carried out by calculating tf-idf with tf-rf using excel calculations to get different values between tf-idf and tf -rf, tf-idf weighting can measure the similarity between documents and keywords of the book that is most similar to the book that is considered the most relevant. So that members enter keywords and then find the similarity of books from the keywords entered previously but for weighting tf-rf provides keywords from each category. The comparison results obtained are 76% for tf-idf and 80% for tf-rf. This system uses the python programming language with the django web framework