Extraction and motion estimation of vehicles in single-pass airborne LiDAR data towards urban traffic analysis

Yao, Wei; Hinz, Stefan; Stilla, Uwe

doi:10.1016/j.isprsjprs.2010.10.005

Cited by 56 publications

(32 citation statements)

References 31 publications

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“…Sometimes hierarchical classification focuses on specific object types. For instance, Yao et al [23] extract vehicles from ALS data for transportation monitoring.…”

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confidence: 99%

SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

2013

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Abstract:Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.

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“…Sometimes hierarchical classification focuses on specific object types. For instance, Yao et al [23] extract vehicles from ALS data for transportation monitoring.…”

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confidence: 99%

SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

2013

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“…During night time or bad weather conditions, active sensors, such as LiDAR [53][54][55] or SAR [56][57][58], may be applicable. Furthermore, infrared sensors are capable of detecting still and recently active vehicles [59].…”

Section: Motivationmentioning

confidence: 99%

An Operational System for Estimating Road Traffic Information from Aerial Images

et al. 2014

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Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks.

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“…For narrowing the search space of vehicle detection geometric data is favorable (Türmer et al, 2013). The LiDAR point cloud can be utilized for extracting vehicles and their motion (Yao et al, 2011). Further features can be estimated by LiDAR point cloud for object classification in general (Jutzi and Gross, 2009;Weinmann et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Investigations on the Potential of Convolutional Neural Networks for Vehicle Classification Based on RGB and Lidar Data

Niessner

Schilling

Jutzi

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks. To study the potential of the Convolutional Neural Network, in this paper three approaches are investigated to train classifiers based on Convolutional Neural Networks. These approaches allow Convolutional Neural Networks to be trained on datasets containing only a few hundred training samples, which results in a successful classification. Two of these approaches are based on the concept of transfer learning. In the first approach features, created by a pretrained Convolutional Neural Network, are used for a classification using a support vector machine. In the second approach a pretrained Convolutional Neural Network gets fine-tuned on a different data set. The third approach includes the design and training for flat Convolutional Neural Networks from the scratch. The evaluation of the proposed approaches is based on a data set provided by the IEEE Geoscience and Remote Sensing Society (GRSS) which contains RGB and LiDAR data of an urban area. In this work it is shown that these Convolutional Neural Networks lead to classification results with high accuracy both on RGB and LiDAR data. Features which are derived by RGB data transferred into LiDAR data by transfer learning lead to better results in classification in contrast to RGB data. Using a neural network which contains fewer layers than common neural networks leads to the best classification results. In this framework, it can furthermore be shown that the practical application of LiDAR images results in a better data basis for classification of vehicles than the use of RGB images.

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Extraction and motion estimation of vehicles in single-pass airborne LiDAR data towards urban traffic analysis

Cited by 56 publications

References 31 publications

SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

An Operational System for Estimating Road Traffic Information from Aerial Images

Investigations on the Potential of Convolutional Neural Networks for Vehicle Classification Based on RGB and Lidar Data

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