Autonomous vehicle technology promises to make our roads safer, whilst increasing the efficiency of transport and delivery services. Safety and reliability are the key factors necessary for the widespread adoption of self-driving vehicles. The algorithms powering cars, trucks, and buses on our roads are required to function perfectly in all circumstances.
In order to navigate obstacles and operate effectively in chaotic real world traffic conditions, computer vision based models must be trained with annotated data that adds information and labels to images and video. For automated vehicles to reach their potential machine learning developers need access to precise training data at large scales.
This blog will focus on the video annotation and show how it is contributing to the development of autonomous vehicles. Collaborating with the video annotation specialists, like Keymakr, is allowing pioneers in this sector to continue the growth of this essential technology.
Video annotation allows information to be added to video data. Annotation tools are used to label objects of interest, or segment pixels in target classes, in each frame of video. Due to the time consuming nature of annotating thousands of frames per video, techniques such as object interpolation are often utilized.
This annotation feature can track and locate objects through multiple frames automatically, increasing the speed and efficiency of the labeling process. Labeling conventions and practices are outlined by machine learning engineers, and communicated to the annotators who then create the training datasets.
Video annotation creates data that replicates the complex movements and interactions present in real world environments. Models trained with effective video data can operate in chaotic traffic conditions, making use of the following capabilities.
- Object detection: Object detection allows autonomous vehicles to identify and respond to specific objects. During video annotation bounding boxes are placed around objects, which are then assigned a label e.g. car, bus, etc. Through exposure to training video annotated in this way computer vision models are able to recognize and navigate around important road objects.
- Object classification: Semantic segmentation helps to contextualize and add detail to video training data. Annotators use annotation tools to assign every pixel in each frame to a particular class, e.g. road, sidewalk, sky. This additional granularity helps self-driving vehicles to travel with precision.
- Lane recognition: In order to be deployed safely on public roads automated vehicle models must be able to recognize lane markings and stay within them. To achieve this video training data is annotated with polylines. These lines can define the parallel shapes of lanes, allowing autonomous vehicles to stay within safe limits.
Finding the right video annotation partner
Video annotation is at the core of effective autonomous vehicle models. However, the annotation process is prolonged and labour intensive, it can often be expensive and distracting for AI companies to establish an effective video annotation operation. Outsourcing to expert providers, like Keymakr, can allow developers to access the following advantages:
- Video annotation tools and platforms: Keymakr has developed a data annotation platform with video annotation as a core capability. Interpolation features on this platform allow annotators to work quickly with this demanding data.
- Managing workflows: Workflow management features can streamline the video annotation process. Multiple annotators can work on one video at the same time, and their work can be seamlessly integrated together.
- Workforce analytics: Keymakr allows managers to view a wide range of performance metrics across the annotation workforce. With the help of these metrics managers can assign work to annotators best equipped to complete it.
It also allows for targeted quality control that can catch mistakes before they make it into final datasets.
Access high quality video annotation
Keymakr provides high quality annotation for autonomous vehicle AI projects. Teams of experienced annotators, supported by rigorous management processes, are able to produce bespoke datasets to meet any need.
Contact a team member to book your personalized demo today.