Abstract
We present a real-time approach for traversable surface detection using a low-cost monocular camera mounted on an autonomous vehicle. The proposed methodology extracts colour and texture information from various channels of the HSL, YCbCr and LAB colourspaces by temporal analysis in order to create a “traversability map”. On this map lighting and water artifacts are eliminated including shadows, reflections and water prints. Additionally, camera vibration is compensated by temporal filtering leading to robust path edge detection in blurry images. The performance of this approach is extensively evaluated over varying terrain and environmental conditions and the effect of colourspace fusion on the system’s precision is analysed. The results show a mean accuracy of 97% over this comprehensive test set.
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Katramados, I., Crumpler, S., Breckon, T.P. (2009). Real-Time Traversable Surface Detection by Colour Space Fusion and Temporal Analysis. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_27
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DOI: https://doi.org/10.1007/978-3-642-04667-4_27
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