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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%.

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.

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Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting 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, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.