Traffic congestion in Makassar has occurred over the last 3 years due to the increase of vehicle amount with the lack of adequate road space. To solve the problem, the Intelligent Transportation System (ITS) is needed. One of the research topics in ITS is determining the turnover time of traffic lights based on the number of vehicles in the road. Viola-Jones method can be used to count the number of vehicles by detecting objects. Therefore, this study shows a simulation of the number of vehicles using the Viola-Jones method as an initial step to implement the ITS in Makassar. The data used is a front view car pictures. Simulation results show that the average accuracy rate of the detection is determined by the number of samples. For example, the average of the highest detection accuracy is 92% by using 150 positive samples and 300 negative samples were applied to 30 test samples.
Cocoa (Theobroma cacao L.) is one of the leading commodities that developed in quality and quantity. Efforts through socialization and counseling in the field of cocoa cultivation for the farmers are always reminding know the importance of identifying symptoms from pest attacks could earlier makeup so that preventive actions that do not damage the environment or agricultural land. The study aims to create an early detection system based on image processing on symptoms of pest attacks on cocoa fruits. The early detection system involves training the data and test the data as a result of capture pictures of ordinary cocoa fruits and reviews those attacked by pests in real time at the location of cocoa plantations. Image processing techniques are integrated into the application software to be Able to identify the pixel characteristics of capture images of cocoa fruits. The results of the study showed that the ability of the system to detect the symptoms of pests in training the data was 100% and the test of data was 70%. This result showed that the applications could be recommended to be developed on a larger scale so that it will be helpful for cocoa farmers.
Abstract-Mixture Model research has been widely implemented for numerous purpose in motion tracking applications. This method usually applied for tracking and counting the vehicles in Intelligent Transport System (ITS). In this context, Mixture Model chosen is Gaussian Mixture Model (GMM) method, due to its powerful features. Unlike many motion tracking-based methods, GMM achieves satisfactory performance from its ability to handle background subtractions. However, its implementation in detecting vehicle still have unsatisfactory result in accuration and identifying object, mainly under heavy traffic condition. The problem turn to poor accuration of object detection. Therefore, in this paper, we propose optimization of GMM performance by adjusting the Region of Interest (ROI). The propose technique to completing the report by compare the result before and after experiment in separate condition. The result show that this approach leads to improvement in tracking and counting average of accuration of motorcycle by 6.97% and car by 39.04% in several condition. Our approach to modified the method has been experimentally validated showing better segmentation performance, and this is an unbiased approach for assessing the practical usefulness of object detection methods for vehicle under heavy traffic condition on the highway.
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