Computer vision for driver protection - a checklist to help you start a successful project

The ability to keep drivers safe is one of the key advantages of computer vision. Autonomous vehicles should be far safer for drivers and other road users in the near future. In the present in-cabin driving aides can help drivers stay alert on busy highways. Driver protection is the goal of many exciting projects in the worlds of automotive computer vision.

However, streamlining development in this sector can be a challenge. AI companies need access to high quality training data that makes models perform better and keep drivers safer. Finding and annotating the right images and video can be a distraction for busy developers.

This blog will identify some of the key steps companies can take to get great datasets for driving protection focused applications.

Firstly, we will look at the use cases that are promising to safeguard drivers. Secondly, we will address the key challenges of data annotation. And finally, we will show how dedicated annotation services can help developers overcome these challenges.

Keeping drivers safe

AI models can keep an eye on the road and inside the vehicle at all times. This extra pair of eyes can monitor complex road situations and keep drivers safe:

• Self-driving vehicles: AI models are capable of object recognition. This allows them to identify important objects, like vehicles, and respond to them in an intelligent manner. This means that AI powered vehicles should be able to recognize other cars, trucks and buses and maneuver around them safely.
  
  Computer vision models also allow autonomous vehicles to stop before they hit pedestrians or cyclists. Because of the important safety implications of this technology it is essential that autonomous vehicles are trained with high quality video and image data.

• Driver monitoring: Drivers can get distracted on the road. They can also suffer from tiredness or be impaired by drugs and alcohol. Smart cabin monitoring systems can recognize when a driver is showing the signs of distraction or impairment.
  
  If these systems spot dangerous signs they can give an alert to drivers and instruct them to pull over. This technology has the potential to save lives. However, behaviour monitoring models like this need annotated data of human expressions and movement in order to function reliably in the real world.

Annotation challenges

The use cases described above depend on precisely annotated training data. However, accusing powerful datasets can be difficult for AI companies of any size:

• Video annotation: Video annotation requires a significant investment of labour and resources by developers. Annotating thousands of individual frames is extremely time consuming and can be a drain on annotation teams.
• Management: Managing annotation projects can be difficult. Especially when workers are crowd sourced and remote. Ensuring that annotations remain precise can be a distraction for busy managers and senior researchers.
• Varied datasets: Powerful driver protection AI models need varied training data. Images and videos taken in different lighting and road conditions helps computer vision systems to be more reliable and adaptable. However, many AI companies can struggle to find or create images and video that fit their specific needs.

Annotation services for driver protection

Keeping drivers safe requires exceptional annotated data for AI training. Keymakr helps developers overcome labeling challenges:

• Annotation tools: Keymakr’s proprietary annotation tools are designed to make video annotation more efficient by sharing video labeling tasks between workers.
• Managed teams: Keymakr has a managed team of experienced annotators who work together to create exceptional datasets.
• Dataset creation: Keymakr’s in-house production facilities mean that automotive AI developers can specify the images and video that they need.

Contact a team member to book your personalized demo today.