The world of professional sports is dynamic and diverse. Computer vision based AI models have shown the capacity to capture the changing states of player movements and game flows, enabling coaches and broadcasters to see more and react quicker. In order for AI models to understand movement it is vital that they are trained with annotated video data.
To create video training datasets, annotation services use annotation platforms to label target objects and individuals in each frame of footage. The information added to videos allows AI models to interpret fast moving sports actions and identify and locate key features.
This blog will look at some of the challenges of video annotation and show the promising applications that it is enabling. Finally we will show how finding the right annotation provider can help streamline sports video data annotation.
The challenge of video annotation
Video annotation is a time consuming and labour intensive process. A five minute video of tv footage can feature over 7000 individual frames. Locating target objects and labeling them in each frame instance takes a significant number of labour hours. As a consequence of the multiplicative effect of video data annotators may be prone to make more labeling errors across thousands of repetitive frames.
The cost and time involved in video annotation makes it challenging for sports AI companies to construct in-house annotation operations. Therefore outsourcing to video labeling experts is usually the most effective way of accessing annotated video datasets.
Use cases enabled by video annotation
Despite the challenges video annotation is an essential process for sports AI developers. Rigorous and conscientious video annotation powers many of the most exciting AI use cases in the world of sports:
- Instant highlights: Highlights packages are essential for the live broadcast experience. However, it can often be difficult for production staff to create and edit highlights within the constricted timescale of a live broadcast, or in the volumes required for web content. Automated highlight applications use AI to identify key moments, rank them in terms of importance and then create highlight videos based on this analysis.
- Analyzing brand visibility: Sponsors pay significant sums to be featured at sporting events, on players shirts, and on advertising hoardings around the stadium. Video annotation allows advertisers to quickly gauge the percentage of time that adverts and logos are visible in live sporting events. Machine learning powered models can locate target ads in each frame of video footage. This helps to guide future ad buys and can drive design and product placement decisions.
- Performance analytics: Machine learning enabled cameras can observe training sessions and provide feedback on performance to coaches. These methods can also be applied to scouting methodologies, enabling teams to spot emerging talent before the competition.
- Detecting injury and illness: Video annotation is an important part of AI applications that monitor player movements. These systems are able to identify unusual movements or behaviour that may indicate that a player is injured or sick. Preventive treatment can be given early thanks to the predictive power of these systems.
Optimizing video annotation in sport
Keymakr is a data annotation provider that specializes in video annotation. By outsourcing to experts sports AI innovators can access key advantages:
- Skeletal annotation: This annotation technique identifies human limbs in each video frame and joins them at points of articulation. This allows computer vision models to understand human movement.
- Object interpolation: Object interpolation accelerates annotation by automatically tracking objects and people through multiple frames.
- Workflow optimization: Keyamkr’s unique workflow features allow multiple people to annotate videos at the same time. Annotations are seamlessly combined to create flawlessly labeled video data in a considerably shorter time.
Contact a team member to book your personalized demo today.