Detection of persons in MLS point clouds using implicit shape models

In this paper we present an approach for the detection of persons in point clouds gathered by mobile laser scanning (MLS) systems. The approach consists of a preprocessing and the actual detection. The main task of the preprocessing is to reduce the amount of data which has to be processed by the detection. To fulfill this task, the preprocessing consists of ground removal, segmentation and several filters. The detection is based on an implicit shape models (ISM) approach which is an extension to bag-of-words approaches. For this detection method, it is sufficient to work with a small amount of training data. Although in this paper we focus on the detection of persons, our approach is able to detect multiple classes of objects in point clouds. Using a parameterization of the approach which offers a good compromise between detection and runtime performance, we are able to achieve a precision of 0.68 and a recall of 0.76 while having a average runtime of 370 ms per single scan rotation of the rotating head of a typical MLS sensor.