Robust vehicle detection in low-resolution aerial imagery

Sahli, Samir; Ouyang, Yueh; Sheng, Yunlong; Lavigne, Daniel A.

doi:10.1117/12.850387

Cited by 14 publications

(10 citation statements)

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“…They successfully detected roads in 96% of the images [11]. Vehicle detection in low-resolution aerial imagery based on SIFT keypoint features and SVN have been proposed by [12,13]. Even though there are several proposed techniques for road and vehicle detection using aerial imagery from recent years [10][11][12][13], we have not found any incident detection algorithm using aerial imagery.…”

Section: Introductionmentioning

confidence: 96%

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: Introductionmentioning

confidence: 96%

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

2012

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“…Samir Sahli, Yueh Ouyang et al [6] proposed a new feature based method for vehicle detection in low resolution aerial imagery. This approach uses scale -invariant feature transform (SIFT) for extract key points in the images.…”

Section: Future Based Approach For Low Resolution Aerial Imagerymentioning

confidence: 99%

Comparative Study on Vehicle Detection Techniques in Aerial Surveillance

Selvakumar¹,

Anbu²

2013

IJCI

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“…Sahli et al [13] make use of aerial images which has average resolution up to 11.2cm/pixel. In this method, the authors explore Scale Invariant Feature Transform (SIFT) [12], which effectively generates all keypoints.…”

Section: Related Workmentioning

confidence: 99%

Ground Vehicle Detection and Classification by an Unmanned Aerial Vehicle

Montanari¹,

Tozadore

Fraccaroli³

et al. 2015

2015 12th Latin American Robotics Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR)

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Abstract-Rotary-wing unmanned aerial vehicles (UAV) are being widely used in different applications due to its several features, such as mobility, lightweight, embedded processing and capability of flying in different height levels. Among the possible applications they are used in surveillance tasks, agriculture environments monitoring, power lines inspections and diseases detection in crops. The images captured by the UAVs need to be examined in most of the applications. This analysis is usually done by experts, but a system that process the captured images in a autonomous way can speed up the results and more images can be processed in a lesser period, which is the ideal for real time applications. This work presents a system for vehicle detection aiming at surveillance task. The system is evaluated in terms of precision of detection of a particular type of vehicle. The video was shot using a monocular camera coupled with an aerial robot developed in our Lab. The experimental results demonstrate that the proposed system can be used for the vehicles tracking task under controlled conditions.

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Robust vehicle detection in low-resolution aerial imagery

Cited by 14 publications

References 14 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

Comparative Study on Vehicle Detection Techniques in Aerial Surveillance

Ground Vehicle Detection and Classification by an Unmanned Aerial Vehicle

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