Professional annotation services, like Keymakr, are collaborating with industry leaders to push forward the development of automated transport infrastructure. In the field of autonomous vehicles high accuracy, high quality data labeling is supporting improvements in reliability and safety. However, automated systems for personal vehicles are just one part of creating a truly safe road network. Increasingly, AI powered monitoring cameras are helping to create a fully integrated and safer environment for all road users. Professional annotation providers are well placed to create and annotate images and video for this developing technology.
This blog will tackle some of the core, emerging applications for computer vision based road traffic monitoring models, and suggest ways in which quality annotation provision can support these important use cases.
Identifying developing traffic congestion
The number of cars on the road is expected to increase to over two billion globally by the year 2040. This explosive growth in road users will put tremendous pressure on existing infrastructure and has the potential to substantially reduce safety. As a result easing congestion and controlling traffic flows is a crucial objective for AI traffic monitoring systems. Models are being developed that have the capacity to identify individual vehicles, and recognise when a traffic jam is developing in real. This information can then be sent to the relevant authorities or even directly to drivers phones, warning them to avoid a certain area.
The machine learning models that enable this responsive monitoring are dependent on quality, annotated videos for training. Professional annotation services make use of experienced annotators to meet demanding data needs and schedules. Accuracy can be guaranteed through responsible management and defined quality control procedures.
Accident early warning systems
Accidents are a major source of congestion, and additional safety hazards. By identifying crashes or incidents early AI monitoring cameras can alert emergency services or steer other road users away from dangerous situations. Accident identification can also support police investigations in the aftermath of a crash. By providing early intervention traffic monitoring systems can reduce the impact of crashes on congestion whilst also preventing future accidents.
Accident scenes are complex and varied. They take place in different weather and light conditions and can involve many different types of vehicles and road users. In order for AI models to correctly identify and interpret road accidents, varied and diverse training imagery is required. Providers, like Keymakr, lean on their experience with a range of complex annotation tasks to create and label images from a range of contexts. They are able to meet developer demands with datasets that avoid bias and represent the complexity of real world traffic.
AI controlled traffic signals
Control of traffic at a granular level is increasingly being given over to traffic monitoring systems. AI backed lights are showing the capacity to regulate traffic flow and, in doing so, create safer driving conditions. Machine learning models can absorb information about traffic level, accidents, and construction work and then use that data to determine the appropriate timings for traffic lights. This facilitates the smooth movement of vehicles through potential congestion points.
In order to perform this kind of complex analysis traffic monitoring systems must be trained with image and video data that delivers pixel level detail and information. Semantic segmentation annotation provides this kind of data, by dividing images into classes object classes, giving a full picture of complex road environments.
Precise, professional annotation for road safety
The exciting developments in AI traffic monitoring promise to make tomorrow’s roads less dangerous and freer flowing. Keymakr’s teams of experienced annotators are part of this drive for innovation and safety. Contact a team member to book your personalized demo today.