Roadway traffic monitoring from an unmanned aerial vehicle

Coifman, Benjamin; McCord, Mark R.; Mishalani, Rabi G.; Iswalt, M; Ji, Yuxiong

doi:10.1049/ip-its:20055014

Cited by 140 publications

(82 citation statements)

References 4 publications

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“…Unmanned aerial vehicles (UAVs) hold promise of great value for transportation research, particularly for traffic data collection (e.g., [1][2][3][4][5]). UAVs have many advantages over ground based traffic sensors [2]: great maneuverability and mobility, wide field of view, and zero impact on ground traffic.…”

Section: Introductionmentioning

confidence: 99%

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

Wang

et al. 2017

Journal of Advanced Transportation

129

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UAV based traffic monitoring holds distinct advantages over traditional traffic sensors, such as loop detectors, as UAVs have higher mobility, wider field of view, and less impact on the observed traffic. For traffic monitoring from UAV images, the essential but challenging task is vehicle detection. This paper extends the framework of Faster R-CNN for car detection from low-altitude UAV imagery captured over signalized intersections. Experimental results show that Faster R-CNN can achieve promising car detection results compared with other methods. Our tests further demonstrate that Faster R-CNN is robust to illumination changes and cars' in-plane rotation. Besides, the detection speed of Faster R-CNN is insensitive to the detection load, that is, the number of detected cars in a frame; therefore, the detection speed is almost constant for each frame. In addition, our tests show that Faster R-CNN holds great potential for parking lot car detection. This paper tries to guide the readers to choose the best vehicle detection framework according to their applications. Future research will be focusing on expanding the current framework to detect other transportation modes such as buses, trucks, motorcycles, and bicycles.

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Section: Introductionmentioning

confidence: 99%

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

Wang

et al. 2017

Journal of Advanced Transportation

129

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“…A pulse-coupled neural filter was used to remove background noise before applying the Hough transform. Coifman et al [7] investigated the use of UASs to monitor roadway traffic to facilitate offline planning and real-time management applications. A feasibility study by Ro et al [8], which conducted a field experiment at a local interstate using UASs, concluded that UAS applications will become popular in the transportation area in the near future.…”

Section: Introductionmentioning

confidence: 99%

Peach Flower Monitoring Using Aerial Multispectral Imaging

et al. 2017

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Abstract:One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Aerial images of peach (Prunus persica) trees were acquired from both experimental and commercial peach orchards in the southwestern part of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (near-infrared, green, blue). The image processing algorithm included contrast stretching of the three bands to enhance the image and thresholding segmentation method to detect the peach blossoms. Initial results showed that the image processing algorithm could detect peach blossoms with an average detection rate of 84.3% and demonstrated good potential as a monitoring tool for orchard management.

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“…The vehicle detection can be used mostly in the automation of monitoring of traffic and large parking lots. Works about the automatic vehicle recognition have long been published, for example (Coifman, 2006).…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection in Aerial Traffic Monitoring

Sincha¹,

Chervonenkis²,

Skribtsov³

2016

American Journal of Applied Sciences

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This work describes a cascade detection of vehicles in Unmanned Aerial Vehicle (UAV) images and videos. There are some new approaches used in the detection. In particular, the Region of Interest (ROI) search is not only based on GIS and navigation data, but also employs visual method based on rapid image segmentation and road detection. The work also suggests doing ROI segmentation by the superpixel technique and trainable four-level cascade detector that uses artificial neural networks as classifiers. Characteristics of the being analyzed regions (combined superpixels) are based on geometric and texture features, as well as on deep features extracted from the image patches by nonlinear auto encoders. To improve the detection quality of the moving vehicles a separate stage of the detector based on optical flow analysis was introduced. Proposed detection algorithm was benchmarked on the real UAV videos and showed the sufficiently high accuracy. Performance of the algorithm allows supposing the on-board usage.

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Roadway traffic monitoring from an unmanned aerial vehicle

Cited by 140 publications

References 4 publications

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

Peach Flower Monitoring Using Aerial Multispectral Imaging

Vehicle Detection in Aerial Traffic Monitoring

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