<![CDATA[Accurate vehicle detection and tracking is required in visual-based systems to perform counting, therefore vehicle detection by utilizing artificial intelligence networks to process image data CNN (Convolutional Neural Network) is introduced, but in implementing CNN several problems arise, CNN models that are often used are better in accuracy and precision, but often use excessive processing power and computing resources to work optimally, they are not compatible with low-budget platforms and systems, and not easy to use on computers without graphics support or NON-GPU computers. In this research, a vehicle counter system is built by implementing pre-trained object detection model, MobileNet SSD and object tracking algorithm MOSSE (Minimum Output Sum of Squared Error). The model and algorithm are implemented with a library for processing digital images, namely OpenCV to build a vehicle counter system with a high comparison value of the predicted number of vehicles and the actual value of the number of vehicles, has low computing power, and can be run on a NON-GPU computer. Keywords: Vehicle Counter System, OpenCV, SSD, MobileNet, MOSSE.]]>
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