While executing a video annotation project, it is imperative to know the steps you would take that would lead to success. The key consideration is the type of tool you choose. To obtain the cost-savings in annotation, you must use a minimum level of automation. Most of the third parties provide automation tools of video annotation that targets particular use cases. You can review the options judiciously and choose the tools that meet your requirement.
There is yet another factor that teams must consider and it is the classifiers. Check if they are consistent throughout the video. Continuity and labelling help in preventing any kind of unwanted errors. You need to be sure to have adequate training data for training your model using the right amount of accuracy. When the AI model processes more labelled video data, there is more precision to predict the unlabelled data. When you consider this point, you have a greater chance of success while deploying.