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Today, metro is one of the urban infrastructure and plays an important role in urban transport. The safety and health of people in a city are always important, and transport in the metro should also be safe. When subway trains operate, it is possible to occur various accidents such as an exit from the rails or collision with a possible obstacle on the rails (human or another train). In this paper, a model is proposed based on RFID technology in which the train is equipped with a RFID reader, and a control circuit with a microcontroller as well as placing RFID active tags at specific points of the path. When the train approaches the tagged points of the route, the train tag reader scans the tag number, and then the microcontroller identifies the status of the environment by retrieving the information from the internal database. Then, the control circuit adjusts the rotational speed of the electric motor and the train speed consequently based on that data. In this way, the speed of the train is adapted automatically and promptly to the conditions and the probability of an accident in unforeseen circumstances is reduced.

Object detection is an important area of research in computer vision. One of the challenges in this domain is to detect objects in real time using the minimum resources possible. In this paper, we describe a robust method for real time object detection that can be used on low-profile hardware and needs little training. This approach is based on a discrete adaptive color thresholding method. By applying a redistribution algorithm based on color specifications on the training data, the system would be able to detect colors that may appear with small changes in lighting conditions in the scene. The detection algorithm uses a spatial voting method to improve the accuracy of the result. These characteristics make this method a robust tool in ubiquitous computing and also help intelligent environments to act/react more properly by increasing their awareness of the environment.

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