Of the Usefulness of Data Labeling in Video Annotation

Video annotation refers to a labeling process used for training artificial intelligence platforms to detect elements in videos. AI-powered tools automate video annotation faster and more accurately than manual annotation. Video annotation is different from image annotation as it captures motion, audio, and objects. It involves data labeling, tagging, and analysis as an input for machine learning and AI models.

Data labeling in video annotation is used for several reasons:

• Better training materials – Well-labelled data in video annotation provides better data for training AI models. The quality of output from data inputs in machine learning, for example, is determined by the quality of the inputs.
• Reduces errors – Video annotation in AI environments is mainly automated. This reduces reliance on humans who are more prone to making mistakes.
• Better results – In search applications, labeling ensures objects with similar characteristics are grouped together, which provides faster results.

Uses of Video Annotation

Organizations use various video annotation tools to generate meaningful information from AI platforms and other applications. Annotations for AI can be used for the following uses:

• Tracking – Artificial intelligence can track the movement of objects in a video. Object tracking is applied in augmented reality, human-computer interfaces, surveillance, and other uses. 
• Location – Video annotations also use AI to find objects and show their location in coordinates or other defined units of measurement. This helps determine the position of objects relative to others. 
• Detection – AI tools in video annotation can detect objects using pre-specified characteristics. For example, a video file can recognize green areas as grass. 
• Segmenting – Video annotation platforms powered by AI can be programmed to categorize objects based on their unique characteristics. For instance, differentiating between trucks and small cars.

Video annotation tools generally guide AI applications to the specific data points we want to analyze. The main aim of machine learning is to provide well-labeled data to allow the model to ‘learn’ by itself and improve its predictive abilities.

Getting Started With Video Annotation

Video annotation can be applied to most businesses, as long as there is a clear value proposition. Solutions like AI work best where they fulfill a function specific to AI. If you are considering using video annotation platforms for any reason, you should consider a few factors:

• Understanding your needs – Why do you need to annotate video data? Is it a long-term or short-term exercise? You may need to acquire the services of video annotation platforms for several reasons. Make sure you do a complete cost-benefit analysis before you begin. Sometimes, it is better to get your own platform rather than outsourcing, for instance, in sensitive data cases. 
• Creating a selection method – Video annotation as a tool for productivity can be optimized depending on the task at hand. A video annotation platform can be chosen based on functionality, application, or efficiency. Some of these parameters may overlap, but it is essential to have a consistent ranking system to get the best available tool. 
• Evaluation and selection – After you have identified the best video annotation approach, it is time to evaluate your needs versus the vendors’ specifications. You want a video annotation platform that fits your needs while being economically viable. In addition, you should consider your level of expertise and the workflow to be implemented.

Applications of Video Annotation Tools

Video annotation is a labeling process that has many broad applications. As the use of AI continues to be integrated into different levels of applications, we expect to see more sectors using AI and video annotation. Currently, video annotation is being applied in the following fields:

• Transport and automotive industries – Modern cars are embedded with many sensors to aid the driver. Video annotation provides learning models to vehicles which helps them navigate by recognizing other cars, people, and objects like bumps, road signs, and traffic lights. 
• Commerce- An ingenious way of applying video annotation. You can use object tracking in video annotation to create a model for how customers interact with products. This information can be used to inform elements like inventory management. 
• Law and order – Although the use of AI in law enforcement is still controversial, we cannot undermine the potential upside. For instance, AI video annotation has successfully integrated into facial recognition software to flag persons of interest. Automation at this level assists the police in handling a potential situation faster. 
• Manufacturing – Video annotation combined with image annotation can be used in robots for manufacturing lines. They can scan and identify defective items and ensure safety regulations are followed. This leads to better quality products, overheads reduction, and a safer work environment. 
• Talent Acquisition – Video annotation can use tracking to create profiles for sports people. In addition to scouts, AI has been used to analyze movement patterns of athletes to measure sprint speeds, throwing ability, and other factors that create world-class sportsmen.

Conclusion

Video annotation is strategically placed as one of AI’s most powerful and flexible tools. AI and machine learning will change how we interact with machines and other human beings. With a broad number of applications, AI has the power to automate most of our tasks and lead to better decision-making for a better quality of life.