Creating a video-based computer vision (CV) application is hard. Video annotation is hard. After spending countless amounts of time and money, data science teams often find that errors or corruption in their ground truth video data has created a spiral of problems throughout their model training process.
In this final part in our series, Building a Video Annotation Platform we explore common challenges specific to video data, explain how these issues can affect downstream model accuracy, and show how Alegion’s video annotation (VA) solution can help prevent any costly surprises due to issues with ground truth video files.
Part 3 Outline:
- Quality annotations start with pristine video
- Encoders v decoders
- The problem with decoders and video annotation
- The challenges of video capture devices and encoding
- How decoders cope with degraded data
- What does it all mean? Most VA platforms handle issues
by forcing pre-processing. Alegion doesn’t.
- How to increase speed, quality, and accuracy of video