Blaser, Stefan

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Blaser
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Stefan
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Blaser, Stefan

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2021 - 20223
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Autor:in: Meyer, Jonas × Institut: Institut Geomatik ×

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  • Vorschaubild
    Publikation
    AI-based 3D detection of parked vehicles on a mobile mapping platform using edge computing
    (2022) Meyer, Jonas; Blaser, Stefan; Nebiker, Stephan [in: The international archives of the photogrammetry, remote sensing and spatial information sciences]
    In this paper we present an edge-based hardware and software framework for the 3D detection and mapping of parked vehicles on a mobile mapping platform for the use case of on-street parking statistics. First, we investigate different point cloud-based 3D object detection methods on our extremely dense and noisy depth maps obtained from low-cost RGB-D sensors to find a suitable object detector and determine the optimal preparation of our data. We then retrain the chosen object detector to detect all types of vehicles, rather than standard cars only. Finally, we design and develop a software framework integrating the newly trained object detector. By repeating the parking statistics of our previous work (Nebiker et al., 2021), our software is tested regarding the detection accuracy. With our edge-based framework, we achieve a precision and recall of 100% and 98% respectively on any parking configuration and vehicle type, outperforming all other known work on on-street parking statistics. Furthermore, our software is evaluated in terms of processing speed and volume of generated data. While the processing speed reaches only 1.9 frames per second due to limited computing resources, the amount of data generated is just 0.25 KB per frame.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Open urban and forest datasets from a high-performance mobile mapping backpack. A contribution for advancing the creation of digital city twins
    (International Society of Photogrammetry and Remote Sensing, 2021) Blaser, Stefan; Meyer, Jonas; Nebiker, Stephan [in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences]
    With this contribution, we describe and publish two high-quality street-level datasets, captured with a portable high-performance Mobile Mapping System (MMS). The datasets will be freely available for scientific use. Both datasets, from a city centre and a forest represent area-wide street-level reality captures which can be used e.g. for establishing cloud-based frameworks for infrastructure management as well as for smart city and forestry applications. The quality of these data sets has been thoroughly evaluated and demonstrated. For example, georeferencing accuracies in the centimetre range using these datasets in combination with image-based georeferencing have been achieved. Both high-quality multi sensor system street-level datasets are suitable for evaluating and improving methods for multiple tasks related to high-precision 3D reality capture and the creation of digital twins. Potential applications range from localization and georeferencing, dense image matching and 3D reconstruction to combined methods such as simultaneous localization and mapping and structure-from-motion as well as classification and scene interpretation. Our dataset is available online at: https://www.fhnw.ch/habg/bimage-datasets
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras. The use case of on-street parking statistics
    (MDPI, 2021) Nebiker, Stephan; Meyer, Jonas; Blaser, Stefan; Ammann, Manuela; Rhyner, Severin Eric [in: Remote sensing]
    A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.
    01A - Beitrag in wissenschaftlicher Zeitschrift