CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: VEHICLE ACTIVITY INDICATION FROM AIRBORNE LIDAR DATA OF URBAN AREAS BY BINARY SHAPE CLASSIFICATION OF POINT SETS W. Yaoa, S. Hinz, U. Stilla

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 VEHICLE ACTIVITY INDICATION FROM AIRBORNE LIDAR DATA OF URBAN AREAS BY BINARY SHAPE CLASSIFICATION OF POINT SETS W. Yao a ' *, S. Hinz b , U. Stilla 3 ‘‘Photogrammetry and Remote Sensing, Technische Universitaet Muenchen, Arcisstr.21, 80290 Munich, Germany b Institute of Photogrammetry and Remote Sensing, Universität Karlsruhe (TH), 76128 Karlsruhe, Germany KEY WORDS: Airborne LiDAR, Urban areas, Vehicle extraction, Motion indication, Shape analysis ABSTRACT: This paper presents a generic scheme to analyze urban traffic via vehicle motion indication from airborne laser scanning (ALS) data. The scheme comprises two main steps performed progressively — vehicle extraction and motion status classification. The step for vehicle extraction is intended to detect and delineate single vehicle instances as accurate and complete as possible, while the step for motion status classification takes advantage of shape artefacts defined for moving vehicle model, to classify the extracted vehicle point sets based on parameterized boundary features, which are sufficiently good to describe the vehicle shape. To accomplish the tasks, a hybrid strategy integrating context-guided method with 3-d segmentation based approach is applied for vehicle extraction. Then, a binary classification method using Lie group based distance is adopted to determine the vehicle motion status. However, the vehicle velocity cannot be derived at this stage due to unknown true size of vehicle. We illustrate the vehicle motion indication scheme by two examples of real data and summarize the performance by accessing the results with respect to reference data manually acquired, through which the feasibility and high potential of airborne LiDAR for urban traffic analysis are verified. 1. INTRODUCTION Transportation represents a major segment of the economic activities of modem societies and has been keeping increase worldwide which leads to adverse impact on our environment and society, so that the increase of transport safety and efficiency, as well as the reduction of air and noise pollution are the main task to solve in the future (Rosenbaum et al., 2008). The automatic extraction, characterization and monitoring of traffic using remote sensing platforms is an emerging field of research. Approaches for vehicle detection and monitoring rely not only on airborne video but on nearly the whole range of available sensors; for instance, optical aerial and satellite sensors, infrared cameras, SAR systems and airborne LiDAR (Hinz et al., 2008). The principal argument for the utilization of such sensors is that they complement stationary data collectors such as induction loops and video cameras mounted on bridges or traffic lights, in the sense that they deliver not only local data but also observe the traffic situation over a larger region of the road network. Finally, the measurements derived from the various sensors could be fused through the assimilation of traffic flow models. The broad variety of approaches can be found, for instance, in compilations by Stilla et al., (2005) and Hinz et al., (2006). Nowadays, airborne optical cameras are widely in use for these tasks(Reinaitz et al., 2006). Yet satellite sensors have also entered into the resolution range (0.5-2m) required for vehicle extraction. Sub-metric resolution is even available for SAR data since the successful launch of TerraSAR-X. The big advantage of these sensors is the spatial coverage. Thanks to their relatively short acquisition time and long revisit period, satellite systems can mainly contribute to the collection of statistical traffic data for validating specific traffic models. Typical approaches for vehicle detection in optical satellite images are described by Jin and Davis, (2007) and Sharma et al., (2006), and in spacebome SAR images by Meyer et al., (2006) and Runge et al., (2007). For monitoring major public events, mobile and flexible systems which are able to gather data about traffic density and average speed are desirable. Systems based on medium or large format cameras mounted on airborne platforms meet the demands of flexibility and mobility. With them, large areas can be covered (up to several km 2 per frame) while keeping the spatial resolution high enough to image sufficient detail. A variety of approaches for automatic tracking and velocity calculation from airborne cameras have been developed over the last few decades. These approaches make use of substructures of vehicles such as the roof and windscreen, for matching a wire-frame model to the image data (Zhao and Nevada. 2003). Despite that LiDAR has a clear edge over optical imagery in terms of operational conditions, there have been so far few works conducted in relation to traffic analysis from laser scanners. On the one hand it is an active sensor that can work day and night; on the other hand it is range senor that can capture 3d explicit description of scene and penetrate volumetric occlusions to some extent. Toth and Grejner- Brzezinska. (2006) has presented an integrated airborne system of digital camera and LiDAR for road corridor mapping and dynamical information acquisition. They addressed a comprehensive working chain for near real-time extracting vehicles motion based on fusing the images with LiDAR data. Another example of applying ALS data for traffic-related analysis can be found in Yarlagadda et al., (2008), where the vehicle category is determined by 3-d shape-based classification. In this paper, a generic scheme to discover the vehicle motion solely from airborne LiDAR data is presented. It is based on two-step strategy, which firstly extracts single vehicles with contextual model of traffic objects and 3d-segmentation based classification (3-d object-based classification), and secondly classifies vehicle entities in view of motion status based on shape analysis. 2. VEHICLE EXTRACTION In this step, we need to at first extract various vehicle categories as complete and accurate as possible, but not considering the difference among them in terms of dynamical status. To Corresponding author.

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