VidiNet is our media supply chain platform where Vidispine customers add and configure different services for their on-premise, cloud, or hybrid environment. In here, you can now access VCS video and image analysis and add this service to your infrastructure – or just your trial account.
The VidiNet Cognitive Services (VCS) is a core architecture designed to manage cognitive services from a growing number of providers on the market. In this first release of VCS, you will find cognitive services based on the AWS Rekognition libraries. With the introduction of VCS, we now take VidiCore API and Vidinet to the next level.
The AWS image Rekognition libraries in VidiNet Cognitive Services (VCS) are able to automatically detect enough information about what is inside your media content to free up human resources to intervene only when necessary. This way, you can not only save on human resources by offloading image detection to computer software but also use your human resources for those unique image recognition tasks only more suitable for manual control.
The VCS video and image analysis will decode the video content and present a list of each found item. All machine learning software depends on confidence values, both for training and executing.
The VCS video and image analysis will provide a confidence value for each item found. This value is essential when designing an architecture that are dependent on the objects, faces, and environments found in the content.
Do you require a confidence value of 90 % or higher for an item to be sure you have the correct data? Or are you willing to lower this confidence value to 60 % to ensure you are not missing an object?
Note that the AWS image Rekognition in VCS will also report back an interpretation of the current event such as “leisure activities “ and is also aware that this is a musical instrument and not only a guitar. The activity in the picture itself is described as “playing guitar“.
And most importantly, VidiCore API, of course, offers the APIs for a UI that will allow for a manual adjustment.