Abstract
The paper presents a computationally efficient method developed to automatically detect obstacles that appear at railroad-road crossings. The proposed algorithm works maintenance-free and automatically detects obstacles that appear and stay or are left at the crossing. These obstacles can pose a threat for the trains causing even a trail derailment. When a detected obstacle is a person it is crucial to generate a warning in order to react and safely remove the person before the train arrives. The proposed algorithm is based on live video stream analysis. The standard image processing methods were used in order to detect obstacles in the acquired images. However, several techniques were proposed to obtain the desired results dealing with image vibrations, heavy rain and snow. Such robustness was obtained by building and maintaining a fuzzy background frame and appropriate filtering. The obstacle detection system was implemented and tested. The results confirmed its correct operation and usefulness in real-world conditions.
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