Computer vision is changing the game in traffic and mobility. It turns visual data into useful insights. This tech helps reduce traffic jams, makes roads safer, and improves city planning.
This article explores how computer vision evolved in time and how it works in real life. It looks at different technologies and what they need to work. It also talks about the results we see from using these systems.
Computer vision has come a long way from being a futuristic concept to a mature technology transforming industries such as healthcare, manufacturing, and – perhaps most visibly – mobility and traffic management. The ability to analyze video footage automatically became possible in the 1960s and 70s, with early work carried out at MIT’s Artificial Intelligence Laboratory. However, it wasn’t until the 1990s that academic and industrial interest in practical video analysis truly accelerated, thanks to advancements in image processing algorithms and increased computing power.
The 2010s marked a turning point, as the rise of deep learning and artficial neural networks (AI) enabled far more accurate detection, classification, and tracking of objects within video streams. Around the same time, we saw the emergence of Edge Computing – a paradigm shift that allowed data to be processed directly at the source (e.g., within cameras or nearby gateways), rather than in distant cloud servers. This significantly reduced latency, bandwidth usage, and dependency on internet connectivity, making real-time video analysis at scale feasible for the first time.
Key milestones on the path to today’s intelligent video systems include:
In European capitals, the density of surveillance cameras varies widely, but estimates suggest an average of 300 to 1000 public surveillance cameras per city, with cities like London and Paris ranking at the higher end. While many of these cameras were originally installed for security purposes, they increasingly form the backbone of modern urban analytics. When upgraded or connected to AI-driven systems, these existing infrastructures can serve as valuable tools for traffic monitoring, congestion management, and safety enhancement.
With the combination of edge computing and intelligent video analytics, the vision of responsive, data-driven traffic ecosystems is no longer theoretical—it’s unfolding on the streets of our cities.
Real-World Applications of Computer Vision in Traffic and Mobility.
Cities are using computer vision to keep an eye on traffic. For example, Barcelona is using AI to make traffic lights smarter. This aims to cut down traffic jams by 20%.
These smart lights adjust based on how busy the roads are. This makes traffic move better and saves time. Kurrant
San Francisco’s SFpark program shows how computer vision helps with parking. It uses sensors and cameras to share parking info in real-time. This helps drivers find spots faster.
This smart pricing strategy encourages parking in less busy areas. It reduces traffic in busy spots. (Source)
Real-time video analytics help spot incidents like accidents or illegal driving. Dubai’s Smart Traffic Systems use this tech to quickly respond to accidents. This makes roads safer. Dubai: Smart traffic system to cover 100% of the main road network by 2026
Computer vision systems are learning to spot pedestrians and cyclists. This helps make traffic signals smarter and safer for everyone. How Copenhagen is Leading the World in Sustainability – The Urbanist
Advanced computer vision systems are now also used to support law enforcement efforts.
Solutions like Fits Traffic’s Computer Vision System enable authorities to monitor
intersections, railway crossings, and ensure road sign compliance in real-time.
These capabilities enhance public safety by detecting violations and potential hazards before they escalate.
Towards intelligent road infrastructure | ITS International
Best Suited For
Periodic monitoring, parking management, license plate recognition
Real-time event detection, movement tracking, incident analysis
Infrastructure Needs
Lower—requires only snapshots or periodic image feeds
Higher—necessitates continuous video feeds and substantial processing power
Cost
More cost-effective due to reduced data and processing requirements
Higher costs associated with data storage and computational resources
Use Cases
Parking occupancy detection, license plate recognition, vehicle counting
Limitations
Limited to static or periodic data capture, less effective for dynamic event detection
Requires significant infrastructure and maintenance, higher complexity
Infrastructure Considerations and Outcomes
Computer vision technologies are key to modernizing traffic and mobility management. By choosing the right technology—image or video analytics—cities can tackle traffic issues. This leads to better safety and more efficient urban transport systems.
