Another great move and improvement of DataFromSky – take a look what the software is now capable of! We can detect and track people even in very crowded environment, reaching the accuracy of detection over 99 %!
The following video is a part of a project supported by EU programme Horizon 2020 called “Study of the individual and collective behavior of people in large-scale events”. The objective is to extract the trajectories of all individuals and to create a pedestrian behavior model for the crowded environment based on the real data from DataFromSky analysis.
This project opens new channels and brings other new possibilities in using DataFromSky in UAV monitoring:
– utilization in perimeter security – active and real-time aerial surveillance in mass events (sport events, concerts, parades, demonstrations etc.)
– detection of non-standard behavior, dangerous situations, suspicious gathering (in demonstrations and crowds)
– studies of pedestrians movement behavior and its patterns
and much more!
Keep a close eye on our website and social pages to follow our progress (Twitter, Facebook, LinkedIn and Vimeo)!
We have recently released a new enhanced version of DataFromSky Viewer! You can download the demo package at the home page. The list of differences includes a lot of things, so let’s take a look at few of them.
New feature? Calculate and export headways!
Have a look on the picture below, how to display the headway data in the Viewer. It is important to have the gates set properly per each lane to get the most accurate data. Once you have all the gates defined, choose one gate in the Traffic Analysis Gates box and click on button “Show” in More Statistics option. In a newly opened window, there is possibility to click on Calculate headways. The system will automatically calculate headways for all defined gates in the video. The headway data can be exported to the CSV file, simply by clicking on “Export”.
Movement Dynamics Graph
We have visualized movement dynamics for each trajectory into a graph containing information about the vehicle’s speed and acceleration (total, lateral or tangential). This graph can be created for each selected trajectory.
Some other improvements…
Added option in CSV trajectory export to select time origin of the exported time stamps. Three options are available (the timestamp will be exported in accordance to the selected option):
Start of Video Sequence
Start of Tracking Log
Start of Trajectory
Rewritten Gap-Time/Time-to-follow analysis to support better fine-tuned parameters
Added dialog for export of Gap-Time/Time-to-follow to enable user provided parameters for the analysis and export
Added manual (in pdf file) explaining the Gap-Time/Time-to-follow analysis
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
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.”
We’re happy to announce another new function – coloring of trajectories according to O/D matrix. This feature completes the O/D coloring theme: You can now select to differentiate by color both the gate pairs’ highlights and individual trajectories.
In other news, DroneDeploy has recently released a report titled Commercial Drone Industry Trends. Predictably, the growth is exponential. Their data is primarily for their business, ortophoto maps – but the growth is spilling into other sectors.
Both the PTW driver and the driver of the vehicle being overtaken are assumed to be rational decision-makers that develop strategies, while commuting in urban environment, trying to get the best outcome for their decisions. These strategies may be cooperative or not based on both drivers’ choices with respect to the distances and safety gaps they leave from the lead vehicle. Since PTW follow unorthodox trajectories, especially in an urban arterial, having detailed naturalistic data is essential, and this is how DFS comes in handy!
We are always looking for partners from academic sphere. Are you a researcher in traffic or civil engineering? Don’t hesitate to contact us!
This video is an excerpt with no motorcycles present. You can see some new features of the Viewer: a lateral acceleration heatmap, distance measurement, and a testing bounding box makes a guest appearance too. Heatmap overlay is a completely new feature: When activated, you can configure it to display a scalar value. You can currently select from various speed, count and acceleration types of quantity. The color map and its value range are configurable as well.
References:
Barmpounakis, Emmanouil N., Eleni I. Vlahogianni, and John C. Golias. “A Game Theoretic Approach to Powered Two Wheelers Overtaking Phenomena.” Transportation Research Board 94th Annual Meeting. No. 15-1425. 2015.
Barmpounakis, E.N., Vlahogianni, E.I., & Golias, J.C. (2016). Intelligent Transportation Systems and Powered Two Wheelers Traffic, IEEE Transactions on Intelligent Transportation Systems, 17(4), 908-916. http://doi.org/10.1109/TITS.2015.2497406
Barmpounakis, E. N., E. I. Vlahogianni, and J. C. Golias. “Vision-based multivariate statistical modeling for powered two-wheelers maneuverability during overtaking in urban arterials.” Transportation Letters: The International Journal of Transportation Research (2015): 1942787515Y-0000000020
We wish to share a couple of interesting features that will be coming to DataFromSky soon. Currently, DataFromSky understands vehicles only as points. This is an outcome of using the particular technology which we chose. However, this results in one very important drawback: We can’t tell real distance between vehicles. Incidentally, we hope for many applications, which would need precise measurement of distances starting from the actual metal, not an ideal point. After this introduction, it should come as no surprise that we are working on detection of vehicle bounding boxes. This will enable much better distance measurement for safety evaluations.
Another improvement in the pipeline is addition of anonymization functions. As you can imagine, aerial recording in urban areas results in a lot of areas being exposed, while they are assumed to be hidden from sight. To protect privacy, DataFromSky will allow marking of areas for blurring or blanking.
We hope to add this soon, so stay tuned for new versions!
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