Any artificial intelligence program is only as smart as the data it is fed. And, annotation is the key to creating such connected and useful datasets. It helps attach critical information to raw data so an AI/ML algorithm can learn about the real world (much like how we teach a child) and develop decision-making capabilities.

That being said, video annotation is much faster than the image, and simpler too. Plus, it has numerous applications for businesses (especially in the streaming-oriented current times). But, before undertaking a video annotation project, it is important to understand how your goal and the process should align.

So, here are five questions that you must keep in mind before getting started with the video annotation project.

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What is video annotation?

A data annotation type- video annotation is the process of adding context-specific tags to video clips. It helps train computer vision AI/ML models in detecting or identifying objects. Unlike image annotation, video annotation annotates objects on a frame-by-frame basis to ensure better recognition by artificial intelligence and machine learning models.

High-quality video annotation brings about validated datasets that ensure pre-eminent AI functionality. Moreover, there are numerous machine learning applications for data annotation type- video annotation between sectors, including crewless vehicles, AI-assisted medical robots, and geospatial technology, to name a few.

6 key questions to ask before starting a video annotation project

Please keep in mind that this is a generic list of questions that you must find answers to before initiating a video annotation project. However, as you go deeper into the project’s intricacies, your queries would look vastly different from the ones listed below, and you will likely need help from a specialist to conceptualize those.

That being said, let’s begin with the key doubts that you should clarify before starting video annotation.

What do you need to annotate videos?

Although this should be quite apparent, most businesses often get stuck on what they need to annotate for effective results. Establishing a connection between your final goal and the raw dataset required to achieve that goal is paramount to a successful video annotation process.

Are your annotation requirements related to a particular domain?

Ahead of labeling/tagging data to train the computer vision AI/ML models, you must comprehend the domain terminology, structure, and classification of the data you plan to use, i.e., build an ontology.

An ontology is the formal naming and definition of any entity’s types, properties, and interrelationships. It exists for a specific domain of discourse. In simpler terms, ontologies give meaning and value to things. You can perceive it as training your AI/ML to communicate using a standard (single) language. Ontologies are imperative to recognize the problem statement and comprehend how computer vision AI/ML can interpret data to solve a specific use case semantically.

How much data is needed for the computer vision AI/ML project?

Usually, any AI/ML is better off being trained on a good enough amount of data. But that volume would depend on your requirements.

For instance, training an automated parking system management model can be done with a few months’ worth of parking lot videos (like 6000 hours). However, in some cases, you can establish specific benchmarks based on the project requirements (e.g., the past five years of the patient’s medical history). This is presumably estimated by having a professional data annotator (with experience in the domain you are targeting) to consistently evaluate the right amount of data needed to train computer vision AI/ML models.

How to handle vast datasets for a video annotation project?

Companies often face challenges when annotating videos in a mass volume. It is, after all, hours of work that requires careful attention but can just as easily get monotonous if not tackled with a professional approach. However, there is no need for businesses to face such anxiety since they have the option to outsource. Most good data annotation service providers offer almost every annotation type and ensure high-quality results.

Which is the best option: outsourcing or in-house video annotation?

Based on a study by Cognilytica, spending on data annotation in-house is five times costlier than outsourcing. Not only is in-house video annotation expensive, but also cumbersome, dominating precious time which could be otherwise spent on other critical tasks.

Still, many companies hesitate to outsource. The main reason for it is security. But that isn’t an issue anymore. Most data labeling companies today take advanced security measures  to address these concerns.

Do you need professional video annotators?

It depends on the complexity, data volume, and expanse of the project. If the project is complex or has niche implications, or handles sensitive data, it is best suggested to hire video annotation professionals to handle it.

While you can use data annotation tools for basic, uncomplicated video annotation projects, a more complex project requires expertise to ensure precision and accuracy.

For instance, interpreting complicated routes for crewless vehicles requires automotive specialists that can recognize and label/tag the ‘right’  information. This equally applies in other sectors like law and medicine, where computer vision AI/ML models training cannot be taken for granted since any mistake in labeling will be transformed into a real-life implication. Even a minute labeling/tagging mistake can be catastrophic in this case.

Conclusion

Data annotation has no boundaries, at least not ones we can see from where we stand at present. However, making the most of this revolutionary technology requires you to know what you want to annotate, how it aligns with your objectives, and which instructions will work well for your artificial intelligence/machine learning algorithm.

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