Project description
Computer vision for traffic management
By enhancing soft transport modes, including cycling and walking, cities are bound to become more sustainable, while contributing to the reduction of pollution levels and traffic congestion. Still, city officials across the European Union are struggling to safeguard vulnerable road users that account for the overwhelming majority of road victims in urban areas. UrbanDynamics aims to re-design mobility models, developing a real-time traffic monitoring and managing system that uses raw traffic data from cameras to recognise, track, and analyse the behaviour of all urban traffic participants, including bicycles and pedestrians. UrbanDynamics will boost growth with revenues surpassing EUR 18 million. At least 15 jobs are also expected to be created.
Objective
The booming urbanisation poses several challenges for cities such as growing traffic volumes, safety, and increasing environmental pressures. Enhancing the use of soft transportation modes (cycling and walking) will make cities more sustainable by reducing congestion and pollution. However city authorities are currently struggling to guarantee the safety of those vulnerable road users: they account for almost 70% of the road victims in urban areas. The objective of increasing the use of soft transport modes can only be achieved with a re-design of current urban mobility models. A holistic approach for managing urban traffic is one the most urgent needs of traffic authorities. Correspondingly, the associated traffic management market is projected to grow at a CAGR = 21% and reach the €19.4 billion by 2022. At ViNotion, a Dutch leading expert in intelligent image interpretation since 2007, we have developed UrbanDynamics, a real-time traffic monitoring and managing system that uses raw traffic data from cameras to recognise, track, and analyse the behaviour of all urban traffic participants, including bicycles and pedestrians. Our solution collects data from pre-existing cameras and by means of machine- and deep learning, analyses the behaviour of all traffic participants providing an in-depth insight into complex urban traffic scenarios. Our competitive advantages rely on the high accuracy, even in broad areas with only one system (false detections < 1% in 18m width lane), and versatility of the traffic information obtained (counting, speed, direction, queue length, waiting time, etc.). At ViNotion we aim at making EU cities more friendly for pedestrians and bicycles and reduce associated traffic fatalities by facilitating an optimal integration of the soft transportation modes into the urban environment. UrbanDynamics will boost our growth with revenues surpassing the €18 million in the 3rd year after market uptake and the creation of up to 15 new position.
Fields of science
- engineering and technologycivil engineeringurban engineeringsmart cities
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- social sciencessocial geographytransportsustainable transportintelligent transport systems
Programme(s)
Funding Scheme
SME-1 - SME instrument phase 1Coordinator
5641 JA Eindhoven
Netherlands
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.