Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Information Acquisition on Pedestrian Movements in Urban Traffic with a Mobile Multi-Sensor System
    ( 2021)
    Borgmann, Björn
    ;
    Hebel, Marcus orcid-logo
    ;
    Arens, Michael
    ;
    Stilla, Uwe
    This paper presents an approach which combines LiDAR sensors and cameras of a mobile multi-sensor system to obtain information about pedestrians in the vicinity of the sensor platform. Such information can be used, for example, in the context of driver assistance systems. In the first step, our approach starts by using LiDAR sensor data to detect and track pedestrians, benefiting from LiDAR's capability to directly provide accurate 3D data. After LiDAR-based detection, the approach leverages the typically higher data density provided by 2D cameras to determine the body pose of the detected pedestrians. The approach combines several state-of-the-art machine learning techniques: it uses a neural network and a subsequent voting process to detect pedestrians in LiDAR sensor data. Based on the known geometric constellation of the different sensors and the knowledge of the intrinsic parameters of the cameras, image sections are generated with the respective regions of interest showing only the detected pedestrians. These image sections are then processed with a method for image-based human pose estimation to determine keypoints for different body parts. These keypoints are finally projected from 2D image coordinates to 3D world coordinates using the assignment of the original LiDAR points to a particular pedestrian.
  • Publication
    Using neural networks to detect objects in MLS point clouds based on local point neighborhoods
    ( 2019)
    Borgmann, Björn
    ;
    Hebel, Marcus orcid-logo
    ;
    Arens, Michael
    ;
    Stilla, Uwe
    This paper presents an approach which uses a PointNet-like neural network to detect objects of certain types in MLS point clouds. In our case, it is used for the detection of pedestrians, but the approach can easily be adapted to other object classes. In the first step, we process local point neighborhoods with the neural network to determine a descriptive feature. This is then further processed to generate two outputs of the network. The first output classifies the neighborhood and determines if it is part of an object of interest. If this is the case, the second output determines where it is located in relation to the object center. This regression output allows us to use a voting process for the actual object detection. This processing step is inspired by approaches based on implicit shape models (ISM). It is able to deal with a certain amount of incorrectly classified neighborhoods, since it combines the results of multiple neighborhoods for the detection of an object. A benefit of our approach as compared to other machine learning methods is its low demand for training data. In our experiments, we achieved a promising detection performance even with less than 1000 training examples.