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Point cloud classification for detecting roadside safety attributes and distances
Detecting roadside safety attributes and distances in point cloud data is a challenging task. The major problems are accurate detection of attributes and attribute centers for calculating safety distance among attributes. In this paper, we propose a point cloud classification framework for safety attributes detection. In addition, we propose an object center approximation technique for distance calculation that has been integrated into the proposed framework. The proposed framework has been evaluated on large real-world point cloud data, and the experimental results are promising. The framework achieved 100% object-wise accuracy on detecting poles and trees, while the overall point-wise accuracy on detecting all seven attributes was 86%.