Towards automatic near real-time traffic monitoring with an airborne wide angle camera system

Rosenbaum, Dominik; Kurz, Franz; Thomas, Ulrike; Suri, Sahil; Reinartz, Peter

doi:10.1007/s12544-008-0002-1

Cited by 37 publications

(28 citation statements)

References 7 publications

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“…On the other hand, if an accident occurs in the middle of a road or in the vehicles intake sides, finding global traffic flow is not useful and global traffic flow shows a normal traffic situation [32,33]. Bottleneck detection is a useful way to find an incident area.…”

Section: Bottleneck Detectionmentioning

confidence: 99%

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

2012

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Abstract. One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.

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Section: Bottleneck Detectionmentioning

confidence: 99%

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

2012

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“…They are defined as: (8) with true positives being the number of vehicles detected, false positives the number of non-vehicle detections and false negatives the number of vehicles missed. In the tracking evaluation, true positives is the number of vehicles tracked correctly, false positives the number of incorrectly tracked vehicles and false negatives the number of vehicles detected, but not tracked (vehicles that are not tracked during image bursts due to occlusion are not counted as false positives).…”

Section: Resultsmentioning

confidence: 99%

“…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

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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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“…art of the proposed vehicle detection based on the antibo detection ( Figure 8), we can distinguish the traffic den mages have been photographed from a same distance mber of vehicles in a specific area [12] and the use of th distinguish the traffic density on the road according to …”

Section: Strategy Of Detectionmentioning

confidence: 99%

Automatic Traffic Monitoring from Satellite Images Using Artificial Immune System

Eslami

Faez

2010

Lecture Notes in Computer Science

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Abstract. Automatic and intelligence Road traffic monitoring is a new research issue for high resolution satellite imagery application in transportation. One of the results of this research was to control the traffic jam in roads and to recognize the traffic density quickly and accurately. This article presents a new approach for recognizing the vehicle and the road in satellite high-resolution images in non-urban areas. For road recognition, they used feature extraction and image processing techniques like Hough transform, Gradient, and thresholding operation and they presented an artificial immune approach to extract vehicle targets from high resolution panchromatic satellite imagery. The average of results is about 94 percent and it shows that the used procedure has the suitable efficiency.

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Towards automatic near real-time traffic monitoring with an airborne wide angle camera system

Cited by 37 publications

References 7 publications

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

An Operational System for Estimating Road Traffic Information from Aerial Images

Automatic Traffic Monitoring from Satellite Images Using Artificial Immune System

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