Posts Tagged: DataFromSky

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DataFromSky joins the conference TIS ROMA 2017

DataFromSky will participate in the International Congress on Transport infrastructure and systems (TIS) held in Rome in April 10.to 12.! You have a great chance to see DataFromSky in action and assess its capabilities. Don’t hesitate and visit the TIS conference in Rome to find out more!

One of the main topic discussed on the conference will be the emissions caused by traffic and their impact on human health. The EU Commission is preparing a strategy for clean transport, which should become effective after 2020 and it is in compliance with the strategy proposal for low-emission transport. One of the significant impulses is the increasing air pollution in cities and a high share (up to one third) of road transport in the creation of greenhouse gases. Nowadays, the total share of transport in the creation of greenhouse gases is around 23% (according to data collected by EUROSTAT).

What we are going to present on the Congress, is the possible approaches to solve this topical issue by using data obtained from the DataFromSky software for modelling of intensities and emission from traffic. This tool uses real traffic information about the monitored traffic area, such as the type of the passing vehicles, speed, acceleration profile or traffic density. Using these mentioned data, application of the DataFromSky software to measure the emission load in cities is currently being dealt with. The aim is to create a supporting tool for traffic control, which is in accordance with the Smart Cities concept and leads to reducing the emission load from traffic. We actively collaborate on this project with top researchers from Institute of Forensic Engineering of Brno University of Technology (doc. Ing. Vladimir Adamec, Csc. & Ing. Barbora Schullerova, Ph.D.) . This issue opens the door for further research and implementation in traffic research.

Other participant of the conference will be Andrea Marella, engineer at TrafficLab, our service partner from Italy. His paper “Implementing traffic simulation models with aerial traffic survey” descibes the method how to obtain both a complex set of data of OD matrix and detailed human driver behaviours data in order to set a specific scenario simulation. DataFromSky tool was involved and used in this research as well.

The whole program of the conference and the list of participants can be found here

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Can DataFromSky be accurate enough for your application?

Our new scientific article answers… Since the inception of DataFromSky we were actively collaborating with academics to analyse traffic and design safer transportations systems for a better future. Beside the development of the processes to extract and analyse trajectories of the vehicles from aerial videos, we also aimed to analyse the accuracy of our approach itself.

In collaboration with Faculty of Information Technology and Faculty of Civil Engineering at Brno University of Technology, we are working to analyse the accuracy of object position estimation and accuracy of extracted trajectories and their properties when estimated by a low flying UAVs. (see our news from a year ago).

In the previous year, we have developed a tool to assess the accuracy of object position estimation algorithm similar to the one used in DataFromSky. We used this tool to estimate the accuracy of object position estimation from aerial imagery captured by a general-purpose drone in various scenes and compared the results with spatial data collected with an industrial grade GPS sensor. A part of this research and its results have been recently published in special issue on Unmanned Aerial Vehicles in peer-reviewed scientific journal International Journal of Transportation Science and Technology and is already available for pre-press preview at the following link: http://dx.doi.org/10.1016/j.ijtst.2017.02.002

The article provides an insight into the nature of the accuracy of position estimation and properties of uncertainty propagation through the algorithm with respect to various aspects of the camera, scene and its setup. The additional contribution of article is to provide a guiding tool to properly choose and set the drone pose and camera to achieve the desired accuracy of the position estimation of objects in the traffic scene prior the capture of the scene itself.

Picture: “Spatial visualisation of the resulting position estimation error in metres caused by non-linear deformation, across the camera field of view. The 4 red crosses represent the images of the landmarks. The camera is situated at position (0,0,100)[m] looking directly down.”

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Connect with us! DataFromSky starts with WEBINARS

Our team of DataFromSky comes up with lots of news, opening online webinars for our customers, potential clients and partners is one of it! We would like to get more in touch with You; therefore, we decided to start sharing our knowledge through a creative way via online web seminars, where everyone can contribute to the debate.

Agenda of DFS WEBINARS:
– Introduction of the project
– DataFromSky Viewer – software tool for advanced interpretation of traffic data
– DataFromSky Traffic drone & Traffic Balloon – special hardware for precise data collecting
– Practical demonstrations of application of DataFromSky in traffic analysis
– Research & Development – detection of near accidents, real-time traffic data
– Discussion

The webinar is opened and higly recommended for everyone, who is interested in learning more about DataFromSky! The only thing you have to do to join the online seminar, is to send your registration via email to the following email address: webinar@datafromsky.com

Please, mention the days, that would be suitable for You. The confirmation with the specification of the date and time block of the webinar will be sent back to your email afterwards.

One online webinar would take around half an hour and one time block is limited for maximum of 7 participants.
First webinars will start from 1. 3. 2017!

Do not hesitate and join our online web seminar to find out more interesting characteristics about DataFromSky.

We are looking forward to connecting on DFS WEBINAR!

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Camera on a balloon for studying traffic in the city of Alba

Flow analysis, speed and critical points in the city of Alba
“DataFromSky”, in other words, data that arrives from the sky through the shootings of a camera placed on a balloon. It was not so difficult to see one of these during these days in the industrial area located in Corso Asti, where the local study “TrafficLab” is going to analyze the traffic from the sky. “This is an analysis that we need for planning the new commerce building placed in this area” says the municipal officer. The results are still processing. Andrea Marella, engineer at TrafficLab, says: “We realized some aerial video at a height about 50/60 meters. The results are really interesting, because it is possible to see the traffic from a different perspective.” And explains: “This is a new methodology developed about three years ago that allows to analyze the vehicle movements from the sky. This service doesn’t interference with the sensitive data, and helps to get information automatically with our software. This can give us the number of the vehicles on the road, the traffic flow, the directions, the speeds and the conflict points.”

Looking for solutions
The camera is attached to a balloon inflated with helium. Marella adds: “We cannot use drones, because it is too difficult to have authorizations. We work in this way from a long time, with motorway companies, shopping centers and traffic local authorities and the results are very satisfactory. In this way we can have a wider vision that allows to report potentially critical situations and to find out how to solve them.”


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DataFromSky & COWI – traffic analysis in Copenhagen

DataFromSky in cooperation with COWI is coming with a new traffic analysis from Denmark! As in all other major cities, massive amounts of traffic goes in and out of Copenhagen every day. This leads to congestion and the everlasting discussion on how to effectively move as many people as possible. In Copenhagen, almost 50 percent of all commuting trips are made using bicycles. This results in numerous potential conflicts between bicycles and cars, and the battle for allocating and distributing sheer road surface space between the transport modes is continually on – especially at certain locations, where available space is scarce. One such place is “Christmas Moellers Plads”, an intersection complex connecting inner Copenhagen to the island Amager. Through the use of a specialized UAV and Data From Sky, COWI has obtained a complete 4-hour overview of traffic passing through this complex for both cyclists and motorists – and the interaction between the two. The data extracted now forms the basis for looking into new and better solutions that will keep this people mover perform at its best.

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Multilevel intersection – no problem for DataFromSky

We never rested on our laurels! Our tool for trajectory interpretation DataFromSky Viewer is being constantly evolved and now comes with few new functionalities!

Multilevel intersection detection
DataFromSky can track vehicles in multilevel roads! As the video shows, we can track all the vehicles even when the visual occlusion appears. DataFromSky can follow the whole trajectory of the car for the tracked time and collect reliable data – average speed, acceleration, gap-time, time to follow, heatmaps or dimensions of vehicles.

Stationary time
Since now, we are able to count the time, for which is the vehicle in a stationary position, in other words, how long the vehicle has to wait until it passes some obstacle (intersection, traffic lights, traffic jam etc.). This figure may help to determine the utilization and load of a traffic junction.

Video parameters:
Hight: 81 m, top-down view
Tool: drone
Place: multilevel intersection, Kato Varibobi – Greece

Take a look at the video below, demonstrating new useful functionalities of DataFromSky!

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Transforming aerial video data into knowledge

DataFromSky is a unique solution for motion analysis of aerial video data. The ability to detect, track and classify moving objects from aerial videos provided by drones, fixed cameras or other video sources opens new possibilities in the field of traffic monitoring. The technology behind is under intensive development and grows more efficient every day through the embedded capability of learning from its mistakes. We are happy to announce that we have made a significant step towards enhanced accuracy thanks to the complex integration of deep neural networks into DataFromSky framework. Our deep neural networks are being trained on millions of samples 24 hours a day. The quality is quite impressive right now!

Our tool for trajectory interpretation DataFromSky Viewer also comes with a new functionality. The new version extracts parameters which are sometimes difficult to obtain, such as gap-time, time to follow, heatmaps, and dimensions of vehicles. We recognize the potential of the data we are able to obtain, and, therefore, co-work on our tools with traffic researchers and traffic engineers worldwide. The video below demonstrates the new functionality of DataFromSky Viewer. Take a look!

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Partnership with Elistair – tethered solution for UAVs

We are proud to announce our partnership with Elistair, from France, which has developed tethered solution for UAVs. Through this partnership, we can deliver a UAV solution for monitoring and analyzing of moving objects from bird’s-eye perspective. We are creating an unique access to unlimited aerial monitoring capabilities, for both public and private entities (traffic monitoring, perimeter security, etc.).

We teach drones to see, to understand image data!

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DataFromSky at Seminar in Liberec – Deep Neural Networks

A Workshop titled Modern methods of image recognition and processing methods was organized recently by Technical University in Liberec. The aim of the workshop was to offer insight into image processing applications from the industry, as opposed to research setting.

Adam Babinec presenting DataFromSky at seminar in Liberec

We presented principles of detection and classification of vehicles from aerial video data using Deep Neural Networks. As you can imagine, Deep Neural Networks are integral part of DataFromSky platform and are used in many subtasks of DataFromSky solution. If you want to know more about the magic behind, you can download the presentation (PDF, 2 MB) and/or contact us.

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Accuracy and error – model and measurements

If you follow our work for a longer time, you have probably noticed that so far we did not provide any exact numbers on accuracy and errors of the measurements. However, a method providing quantitative data without any quantification of its precision is somewhat dubious. Thus it should come as no surprise that we were working on these matters zealously behind the scene.

So, what do we have now? We have created a model of the whole process which generates our data, and we are currently working on a set of measurements to validate it.

Model

We have created a mathematical model of the whole process which generates our data. This includes the physical reality at target, complete optical system, and digital processing. The following error sources were considered:

  • landmark location errors (in meters)
  • landmark pixel uncertainty (in pixels)
  • camera intrinsic parameters (in pixels)
  • target pixel uncertainty (in pixels)
  • air turbulence (in pixels – included in target/landmark pixel uncertainty)

Using this model, we were able to relate together many variables of the setup – achieved accuracy, distance, covered area, incidence angle, slant range… The results were encouraging. The following picture shows area covered when using a 4k camera, depending on incidence angle and slant range, assuming maximal error of 0.5 meters:

Accuracy for a 4k camera, depending on incidence angle and slant range

What can one read from the chart? As you can see, the incidence angle of about 40° is a reasonable cutoff value. Slant range of 140 meters at 0° (i.e.: directly overhead) gives the best value. For a HD camera, the area covered is a quarter of that for 4k, and optimal altitude in zenith is halved – 70 meters.

We can also overlay the model’s predicted accuracy onto real pictures – that is, display achieved accuracy along with the footage. We hope to eventually incorporate that functionality into DataFromSky Viewer, so that you could check yourself. For now, we have this picture from the Randers video (in HD). Numbers are error in meters, with respective isolines displayed. A 4k video would yield half the error.

Achieved accuracy example in a real scene

Validation measurements

In order to validate the model, we made a set of measurements at a suitable place near Popice, a small Southern Moravian village known by the vineyards in the area.

Popice area used for measurements

We placed a regular grid of 64 landmarks in an 8×8 square pattern, so that a side of the square was exactly 100 meters. The landmarks were positioned using a professional GPS in differential mode, achieving placement accuracy of about 5cm.

Split picture: Calibration landmark, Jiří Apeltauer holding the gps receiver

Then, we set up an UAV to fly around and take a video, in 4k of course. Here is the trajectory projected onto ground, looking at the area from the west.

UAV trajectory projected onto ground

We simply imported the recorded video into DataFromSky and added the landmarks as tracked objects. You can’t see them in the picture because the red ID label “pin heads” are larger than an A4 at that resolution, but they are there.

UAV trajectory projected onto ground

Results

We are still working on processing the results. So far, the agreement between model and measurements is very good, and the model output suggests accuracy greater than we hoped for!

We will publish the results in an academic journal paper. Hopefully, the paper will be finished in a few days and we will be able to share more!

Since this text is about accuracy, we can hint that there is more to come: We measured the vehicle position using a vehicle-mounted dGPS as well, so there is be another set of data to work on.

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