PART B: VALORISATION PLAN
|Please provide a short description of the state-of-the-art and/or current trends in the field? How does the result fit into it?|
|There are many techniques currently in use to monitor the traffic of vehicles on the roads.
The most common way to monitor the traffic is based on sensors embedded in the asphalt, the inductive loop detectors (ILD). On the road, also magnetic sensors, radar sensors and video cameras are frequently found. In some cases, vehicles (e.g. trucks on certain categories of roads) are equipped with radio transceivers able to communicate with systems used for automatic vehicle identification (and billing). All of the mentioned systems are more suitable to certain application or data to be collected and defective in some other areas. ILDs are cheap if installed during road construction, are unaffected by whether and, at the same tine, they give back only some basic information about a crossing vehicle.
Radar systems may provide per-lane presence as well as volume, occupancy, speed and classification information but may be affected by some adverse whether conditions. The same is valid for cameras if used in conjunction with algorithms for vehicle detection.
Probably the most effective system is the one integrating the information coming from more sensors using different technologies.
Video image processing, also known as machine vision, is a technology that captures and analyzes video images through sophisticated algorithms using off-the-shelf components and dedicated hardware and software.
The solution proposed is in the branch of machine vision and involves the use of static cameras observing a dynamic road scene. Cameras are placed on posts above the ground to obtain optimal view of the road and the passing vehicles. The video input is analyzed by algorithms capable to add to simple detection of vehicles other information useful for traffic management tasks.
In addition the R&D result provide some adaption capabilities to changes in the environmental conditions (such as changing light levels, i.e. night-day, sunny-cloudy), to changes in the scene’s content (such as changing camera viewing position), and to changing analysis goals (i.e. traffic flow to counting or occupancy measurement). To summarize, the target of the R&D result is to implement an adaptive video-based traffic monitoring system providing robust vehicle detection, classification and tracking.
|What is the problem/need/knowledge gap that the research result is responding to? How was it addressed before?|
|Keywods: real time analysis road traffic, vehicle traffic flow monitor
The need is to implement a video-based traffic monitoring system. The proposed solution improves the robustness of detection and reduce false detection rates, employing tracking techniques. Tracking allows individual vehicles to be identified as unique targets and their movements can be followed. While detection mimics the operation of inductive loop detectors, vehicle tracking can yield traditional traffic parameters such as traffic flow and velocity but also new parameters such as lane changes and vehicle trajectories. The additional information from the vehicle trajectories can moreover lead to improved incident detection, both by detecting stopped vehicles within the camera’s field of view and by identifying lane change maneuvers or acceleration/deceleration patterns that are indicative of incidents beyond the camera’s field of view.
From a web search several solutions are available for the market of traffic management systems, some of them are listed below.
Autoscope ( http://autoscope.com/products/ – Image Sensing Systems, Inc.) video vehicle detection systems can be easily configured for a variety of applications and installation is easier and safer than loops. Video and data can be transmitted to virtually anywhere using any number of communication technologies. Autoscope systems are ideally suited for junction control, traffic data collection on roadways and incident management applications for bridges, tunnels, and highways, as well as surveillance applications. The Autoscope Solo Terra sensor consists of an integrated color camera, zoom lens and machine vision processor all in one compact unit. The performed tasks are vehicle detection, traffic data measurement and incident detection.
Traffic Master ( http://www.trafficmaster.co.uk/content/1/60/real-time-traffic-information.html ) Trafficmaster is a provider of real-time traffic information in the UK. Infra-red spot speed measuring sensors are deployed on Motorways, and automatic number plate recognition technology is used to measure journey times on Trunk Roads. No information about sensors and algorithms is given.
Iteris’ Vantage ( http://www.iteris.com/vvd.aspx ) Combining industry-leading performance, functionality, and ease of use, Iteris’ Vantage video detection systems offer winning solutions to address a broad range of vehicle detection applications. The systems capture and analyze video images through sophisticated algorithms, providing accurate vehicle detection in a flexible and easy to implement package.
Traficon ( http://www.traficon.com/page.jsp?ref=principles ) A video camera is installed at a certain height for monitoring the traffic. Its video signal is used as input for the detection unit. In a typical Traficon installation, a detection unit consists of a number of VIP (Video Image Processor) boards integrated into a standard 19″ rack (together with 1 communication board). During set-up of a VIP, detection zones are superposed on to the appropriate position in the video image. As a vehicle crosses these detection zones thus activating them, the vehicle is being detected. Application specific algorithms provide different types of traffic information. Traffic data for statistical processing: Incident related data, presence data. The Traficon PC Software (TMS, Flux) monitors the video detection system in the traffic control center.
|What is the potential for further research?|
|The potential is represented by the discover of new cases in which current algorithms are showing poor performance and integrating videos coming from different cameras to infer more complex information.
|What is the proposed method of IPR-protection? (patent, license, trademark etc.)|
|The research result is a software and it is not a stand-alone product, thus only the copyright is applicable.
|What are the steps that need to be taken in order to secure the IPR-protection? What is the cost of IPR-protection?|
|About the protection of the algorithm, the copyright could be considered.
Copyright protection by the registration of the software on the Public Register for computer programs has a cost starting from 120 €.
A similar level of protection can be made by the researcher by sending the software description and the software itself on a CD in a closed envelop by certified mail to himself. The stamp certifies the date of release of the software and can be used against claims of originality or to claim originality.
In any case the patent issue has to be carefully investigated. In fact, patents related to the project keywords are shown by common patent search engines. Some examples are reported at the following links.
Existing patents cover both methods and machines to be used in traffic flow detection or vehicle tracking.
|What is you overall assessment of the scientific maturity of the research result?|
|At scientific level the research result is at good level of maturity. Some issues may arise with existing (and possibly patented) products or methods.
KEYWORDS QUANTITATIVE ASSESSMENT (0-5).
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