Junbeom Jeon, Heezin Lee, Sangyoon Oh, Minsu Lee

KIPS Transactions on Software and Data Engineering, Vol. 5, No. 8, pp. 369-376, Aug. 2016
10.3745/KTSDE.2016.5.8.369,   PDF Download:
Keywords: LiDAR Data, Vehicle Detection, Vertical Elevation
Abstract

Among various vehicle extraction techniques, OBPCA (Object-Based Point Cloud Analysis) calculates features quickly by coarse-grained rectangles from top-view of the vehicle candidates. However, it uses only a top-view rectangle to detect a vehicle. Thus, it is hard to extract rectangular objects with similar size. For this reason, accuracy issue has raised on the OBPCA method which influences on DEM generation and traffic monitoring tasks. In this paper, we propose a novel method which uses the most distinguishing vertical elevations to calculate additional features. Our proposed method uses same features with top-view, determines new thresholds, and decides whether the candidate is vehicle or not. We compared the accuracy and execution time between original OBPCA and the proposed one. The experiment result shows that our method produces 6.61% increase of precision and 13.96% decrease of false positive rate despite with marginal increase of execution time. We can see that the proposed method can reduce misclassification.

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