20,972 research outputs found
Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery
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%
Spatio-temporal road detection from aerial imagery using CNNs
The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we
propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In
order to train this neural network, we have put together a database containing videos of roads from the point
of view of a small commercial drone. Additionally, we have developed an image annotation tool based on
the watershed technique, in order to perform a semi-automatic labeling of the videos in this database. The
experimental results using our modified version of SegNet show a big improvement on the performance of the
neural network when using aerial imagery, obtaining over 90% accuracy.Postprint (published version
Texton Based Segmentation for Road Defect Detection from Aerial Imagery
Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and
Pattern Recognition Workshops, 201
Automatic Building Detection in Wide Area Imagery
Unauthorized construction can cause damage to public and private infrastructure, including utilities, public housing, telecommunication equipment, etc. Current construction analysis is performed by human analysts, who can become fatigued after reviewing large amounts of imagery and are expensive to employ. In order to improve efficiency and reduce cost in monitoring this unauthorized construction, there is a need for automating the detection of regions of interest in imagery. In this work, we focus on the automatic detection of buildings. Sources of aerial and satellite imagery can be used as sources of data in order to perform these detections. While standard visible imagery with red, green, and blue channels may be used, additional information can be extracted through the use of infrared data. In this research, we have created a building detection algorithm that utilizes texture, shadow, road, and edge information for use in detecting buildings from visible and infrared imagery in rural, suburban, and urban areas. Several examples of real-world satellite imagery are used in order to evaluate our building detection algorithm.https://ecommons.udayton.edu/stander_posters/1803/thumbnail.jp
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