Video annotation involves a process that helps computers to learn and recognize objects. It analyses, marks and labels video data. The video annotation practice accurately identifies and labels video footage. The process is quite similar to image annotation as both methods look for training computers to imitate the insightful attributes of the human eye.
The video annotation projects combine automated tools and human annotators that label specific items in video footage. Thereafter, the labelled footage is processed by the AI-driven computer as it discovers the tactics of identifying target objects through machine learning (ML) in a new and non-labelled video. The performance of the AI model is splendid when there is accuracy in the video labels. The accurate video annotation helps companies in confident deployment and quick scaling through the automated tools.
When you annotate videos instead of images, it pays great dividends in terms of the robustness of the training dataset outcomes and also makes the overall process more efficient. If developers use the right techniques, video annotation can be highly advantageous. Video annotation needs less human intervention and labour and hence the time taken for annotation is lesser.