A Traffic-sign recognition IoT-based Application

International data corporation predicts that 21.5  billion connected Internet of Things (IoT) devices will generate 55% of all data by 2025. Nowadays, camera sensors can be embedded in most devices. Therefore, we designed an application to receive a video stream from a camera sensor and perform the video processing. First our designed application pre-processes the sensed data by two high-quality video encoding and framing frameworks. Afterward, we apply the machine learning  (ML) model based on the low and high training accuracies. Because the user devices cannot often perform high-load machine learning training operations, we consider the ML inference operation acting as a lightweight trained ML model. At the end, the processed data is packaged for the consumer such as the driver of a car.