LUMOS - airborne traffic monitoring system

Ernst, Ines; Sujew, S.; Thiessenhusen, Kai-Uwe; Hetscher, M.; Rassmann, Sebastian; Ruhé, Martin

doi:10.1109/itsc.2003.1252052

Cited by 16 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the cell ID can be retrieved to query the mapping table and to get the geographic location of the queried cell when the CA signal occurs, and the handoff-pair can be retrieved to query the mapping table and to get the geographic location of the queried handoff-pair when the HO signal occurs. When two or more locations of the same MS are determined by database lookup methods, the average vehicle speed and travel time between these locations can be estimated [20]. Birle et al [21] recorded two handover signals to measure the retention period of each cell, and the time difference between handover signals and the length of the road segment covered by the cell could be analyzed for the estimation of vehicle speed.…”

Section: Location Servicesmentioning

confidence: 99%

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

2016

View full text Add to dashboard Cite

Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.

show abstract

Section: Location Servicesmentioning

confidence: 99%

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

2016

View full text Add to dashboard Cite

show abstract

“…Several projects that contribute to area-wide traffic monitoring by remote sensing based on airborne optical and SAR sensors are currently running at the German Aerospace Center (DLR) or have already been concluded (e.g., [6][7][8][9]). Meanwhile, an airborne optical system for traffic monitoring with real-time capability has been developed [10].…”

Section: Motivationmentioning

confidence: 99%

An Operational System for Estimating Road Traffic Information from Aerial Images

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

“…Airborne imagery provides a high spatial resolution combined with acceptable temporal resolution depending on the flight repetition rate, but require complex image analysis methods and traffic models to derive the desired traffic parameters. Some work has been done in this field already using image data sources from smaller frame cameras [7,17]. A processing chain for extraction of traffic parameters from 3K camera images in near real time containing road segmentation, vehicle detection, and vehicle tracking is currently under development.…”

Section: The Module Traffic Monitoringmentioning

confidence: 99%

A new software/hardware architecture for real time image processing of wide area airborne camera images

Thomas

Rosenbaum

Kurz

et al. 2008

J Real-Time Image Proc

View full text Add to dashboard Cite

This paper describes a new software/hardware architecture for processing wide area airborne camera images in real time. The images under consideration are acquired from the 3K-camera system developed at DLR (German Aerospace Center). It consists of three off-theshelf cameras, each of it delivers 16 Mpixel three times a second. One camera is installed in nadir, whereas the other two cameras are looking in side direction. Main applications of our system are supposed to be automotive traffic monitoring, determining the workload of public road networks during mass events, or obtaining a survey of damages in disaster areas in real time. Altogether, this demands a fast image processing system on the aircraft, because the amount of original high resolution images can not be sent to ground by up-to-date transfer mode systems. The on-board image processing system is distributed over a local network. On each PC several modules are running concurrently. In order to synchronize several processes and to assure access to commonly used data, a new distributed middleware for real time image processing is introduced. Two sophisticated modules one for orthorectification of images and one for traffic monitoring are explained in more detail. The orthorectification and mosaicking is executed on the fast graphics processing unit on one PC, whereas the traffic monitoring module runs on another PC in the onboard network. The resulting image data and evaluated traffic parameters are sent to a ground station in near real time and are distributed to the involved users. Thus, with the here suggested software/hardware system it becomes possible to support rescue forces and security forces in disaster areas or during mass events in near real time.

show abstract

LUMOS - airborne traffic monitoring system

Cited by 16 publications

References 0 publications

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

An Operational System for Estimating Road Traffic Information from Aerial Images

A new software/hardware architecture for real time image processing of wide area airborne camera images

Contact Info

Product

Resources

About