Road recognition in poor quality environments for forward looking buried object detection

In this paper, we propose a reinforcement random forest algorithm as a novel approach to detect unpaved road regions at stand-off distances. A random forest classifier is used to differentiate between road and non-road pixels/patches without over fitting the training data. Utilizing a reinforcement technique, the algorithm can handle foreign objects that we encounter in real world driving. Furthermore, classifying road patches at different distances generates multiple levels of road agreement for each pixel within the image. Using different threshold values of this agreement level provides adaptability to the road finding results. The selection of low threshold values produces better detection rates but also increases false alarms. On the other hand, high threshold values lower the detection rate and decreases false detections. In our experiments, the proposed algorithm is tested on color video of unpaved road in an arid environment.

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