Vehicle detection using background subtraction and clustering algorithms

Abstract

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.

Threshold adaptation and XOR accumulation algorithm for objects detection

“…The Gaussian noise is removed to the Bf(r, c) by Gaussian filter which makes use of (k×k) size aperture selected through experimental results as (5). It should be observed that median filters and Gaussian filters have a point view; the center location values may not derive (not originate) from the pixel values in the source frame when the median filter's location values are derived from the original pixel values of the source frame.…”

UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

“…In order to determine the accuracy, we use the confusion matrix as in Table 1 [22]. The formula for calculating the accuracy is shown by (3).…”

“…The aim is to get the best model that could recognize TELKOMNIKA Telecommun Comput El Control  UNet-VGG16 with transfer learning for MRI-based brain tumor … (Anindya Apriliyanti Pravitasari) 1311 the tumor area more precisely. The previous studies use the clustering as the basis of segmentation are performed by [2] which uses the genetic algorithm and [3] which employ fuzzy clustering, Otsu method and K-means cluster to segment the vehicle image. The model-based clustering is performed by [4][5][6] in the form of a Finite mixture model to segment the MRI brain tumor image.…”

Searching surveillance video contents using convolutional neural network

“…Moreover, VCA is of high importance in subway stations to detect dangerous situations and secure the areas [3]. Transportation systems rely on VCA to ensure passengers security, vehicle control, and better tracking methods [4]. Furthermore, video analysis is used to detect underwater objects [5].…”