The Challenges and Opportunities of Annotating Videos for Computer Vision

Published On December 09, 2021 -   by

In today’s business landscape, artificial intelligence (AI) is playing a critical role. From automating complex tasks to filtering through vast amounts of data, AI is giving businesses a revolutionary way to get ahead of others in a highly competitive market. That brings us to computer vision, an essential research tool used in the development of AI.

Computer vision utilizes various techniques to help AI systems learn to recognize visual elements. By perfecting it, machines are able to see data in a new light and improve our lives in ways that we didn’t think were possible before.

One of the ways in which computer vision can be trained for AI is through the use of video annotation. Self-driving cars, advanced VR, and checkout-free retail are all made possible through the power of computer vision driven by video annotation.

Challenges to Video Annotation for Computer Vision

Let’s take a look at some of the challenges that companies face with video annotation.

1. Dealing with Large Datasets

In order to fully realize computer vision-based AI systems, a monumental amount of training data is required. However, huge datasets come with their own unique challenges. The large-scale datasets required to properly train AI systems can overwhelm some businesses, leading to wasted time and effort.

2. Consistency and Accuracy

While large datasets are the most challenging aspect of video annotation, accuracy comes in as a close second. In most cases, inaccuracies are the result of a company having to struggle with a large volume of data. These flaws in consistency cause prediction models to be filled with inaccuracies. This is why it’s so important for businesses to bring in experts that can successfully handle their data load.

3. Training and Testing Data

Prediction models are only as accurate as the data they receive so it’s essential that companies generate high-quality training data. Training quality data isn’t quite enough. It must also be accurate. The two main types of data are subjective and objective, both of which can have inaccuracies that can only be filtered out by skilled analysts.

4. Choosing the Right Service Provider

To get high-quality results, businesses must partner with a service provider who has the right experience in delivering video annotation services. The challenge is that there are so many providers that businesses can struggle to sift through the choices and zero down on the right partner who understands the nuances of video annotation and has proven expertise.

Opportunities of Annotating Videos for Computer Vision

1. 2D and 3D Object Detection for AR and VR

The purpose of video annotation is to capture objects so that machine learning systems can recognize them. This makes it the perfect candidate for improving AR and VR systems. Video annotation tracks objects like people, cars, and even plants so that VR systems can accurately predict their movement. This creates a more realistic environment.

2. Surveillance

Security systems gain an advantage by utilizing machine learning processes. Surveillance systems can automatically identify specific behaviors. Data is gathered pertaining to human behavior and fed into AI systems so that odd patterns can be identified. Video annotation is used on normal footage to achieve this goal.

3. Tracking Buyer Behavior in Retail Settings

Another use of video annotation is in retail. We see companies like Amazon incorporating automatic checkout in many of their physical locations. This is all made possible through machine learning systems. Object detection, buyer behavior, and other essential data are localized so that these systems can predict what’s happening. Of course, it takes a large volume of data to achieve this.

4. Autonomous Vehicles

Another amazing opportunity that we’re seeing in the 21st century is the rise of fully autonomous vehicles. These systems are trained using advanced video labeling processes. Pedestrian behavior, street lights, and vehicle movement are all tagged in videos and then fed into these systems so it learns to predict real-world behavior on the road.

Conclusion

Industries around the world are being changed through the development of machine learning so companies have to adapt to stay competitive. Training models are being driven by computer vision but that’s not without its challenges.

DataEntryOutsourced (DEO) is a highly experienced provider of video tagging and annotation services and can help your business accurately label and classify a large number of videos affordably. Contact DEO today for a free quote.

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