Outdoor Mobile Mapping and AI-Based 3D Object Detection with Low-Cost RGB-D Cameras: The Use Case of On-Street Parking Statistics

Abstract

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.

Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data

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“…(4) In the middle of the training architecture, certain intermediate classifier branches, such as the Auxiliary classifier, are introduced. This mechanism provides regularization and helps to avoid the vanishing gradient issue [104,105].…”

Exploiting Pole-Like Objects From Cadastres for Sub-Metre Accurate Integrated Georeferencing of Low-Cost Mobile Mapping Systems

“…In addition, government agencies are interested in freeing street space, including areas currently occupied by on-street parking, to accommodate new bike lanes and other alternative modes of transportation. To provide automated, reliable base data in form of on-street parking statistics for government agency and policy makers Nebiker et al (2021) developed a low-cost Mobile Mapping System (MMS) to cover entire cities with regular revisit capabilities. The system uses point clouds from range imaging sensors, edge computing and AI-based 3D object detection methods to detect vehicles directly on the MMS (Meyer et al, 2022).…”

“…The system uses point clouds from range imaging sensors, edge computing and AI-based 3D object detection methods to detect vehicles directly on the MMS (Meyer et al, 2022). However, reliable on-street parking statistics require accurate georeferencing of the detected vehicles at the dm-to sub-metre-level (Nebiker et al, 2021). Direct georeferencing based on GNSS/INS sensor integration is the current technology of choice.…”

Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module

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