Computer vision AI models are changing cars for the better. Autonomous vehicles are making driving safer, whilst improving the comfort and convenience of road travel. As a result, this technology is becoming a more common sight on roads across the world. In-cabin monitoring systems also rely on computer vision AI to help keep drivers safe.
Behavior monitoring systems can identify when a driver is falling asleep or otherwise impaird, a vital capability that can save lives. Video annotation is essential for these important applications. By labeling each frame of video annotators create training data that helps autonomous vehicle AIs interpret movement accurately and reliably.
However, there are a number of challenges that need to be overcome in the video annotation process. Annotation tools, like Keylabs, can make video annotation easy and overcome bias in training data at the same time. Particularly by making object interpolation accessible for AI developers.
Firstly, this blog will look at the difficult challenges facing video data creationI in the automotive industry. Secondly, we identify the key AI applications that are made possible by precise video annotation. And finally, we will show how the right video annotation tool can improve video interpolation.
Dataset problems for automotive AI
Autonomous vehicles need high quality, varied training data if they are to perform optimally and safely. The following challenges need to addressed by developers in this sector:
- Varied road markings and signage: Road signage and markings vary from country to country. This can make it hard for AIs trained in one setting to understand diverse speed limits and driving conventions. AI training video should be taken from different road systems in order for self-driving vehicles to perform well.
- The driving environment: Different regions also have different driving conventations. Overtaking conventions often differ, as does which side of the road is used. In addition, diverse driving behaviors can affect autonomous vehicle performance.
- Image and video quality: The quality of training images and video can significantly impact autonomous vehicles. In order to perform with the highest degree of precision autonomous vehicle AIs need to be trained with high definition images and video that reflect the full complexity of the real world.
- Lighting and weather conditions: Drivers often need to travel in low light and in poor weather conditions. Therefore, if autonomous vehicles are only trained with images and video taken during the day in high visibility conditions they may be less functional in “non-ideal” scenarios.
Key AI capabilities
Despite the challenges, video annotation is essential. The following use cases are at the core of the self-driving revolution:
- Autonomous vehicles: Fully autonomous vehicles can dramatically reduce accidents and make traveling a more relaxing experience. The right video data annotation will ensure that consumer confidence in this technology grows over the coming years.
- Behavior monitoring: In-cabin cameras can monitor drivers and analyze their behavior. Video training data allows AI models to identify when a driver is falling asleep or impaired in another way. Computer vision systems can then alert drivers and instruct them to pull over, preventing accidents.
Annotation tools and interpolation
Data annotation tools can transform video labeling for autonomous vehicle projects. Object interpolation features are an important part of video annotation. Algorithms allow objects, like cars, to be tracked from frame to frame automatically.
Annotators locate a vehicle in a frame of training data with a bounding box, they then move the footage forward and locate the same vehicle in a later frame. Object interpolation then automatically locates the vehicle in all of the intervening frames. Interpolation is vital for speeding up the laborious process of video annotation.
Keylabs is designed by annotation experts to streamline annotation. Keylabs features a full suite of interpolation options. Keylabs also allows multiple annotators to work on the same video simultaneously, and then integrates their annotations seamlessly. Automotive AI developers can transform their video annotation process with Keylabs.