Machine and deep learning use image and video annotations to classify, tag, or label images and videos using annotation tools. You can use image and video annotation to train machine learning algorithms to detect and recognize the labeled or classified segments independently. Video and image annotation helps streamline business operations. It ensures the security of the business, and you can also use it in product labeling, sales, and tracking.
Trained Artificial Intelligence (AI) models eliminate the need for human annotators, reducing the money you spend on salaries and other team member compensations. It also increases the accuracy of the videos and images, meaning you can easily spot misplaced objects or any questionable activities around the business.
Image and video annotation operate similarly, but they differ in that video annotation uses frame-by-frame image data. However, many image and video annotation trends have emerged with the changing technology. If you’re running a modern business that uses AI for facial recognition, data processing, product tagging, or character animation, you’d want to keep up with these trends to ensure you remain on top of the competition. That said, here are four emerging trends in image and video annotation as used in businesses:
1. Greater Use Of AI-Powered Annotation Tools
One of the current trends in image and video annotation is the increased use of Artificial Intelligence-powered annotation tools. These AI tools classify objects automatically, allowing you to automate processes and perfect object recognition and image labeling.
Automating these processes allows a fast, convenient, and more efficient image and video annotation. An excellent example of businesses that use these tools is security firms. They use image and video annotation to analyze security footage and to identify and track suspects. If you’re wondering why you should use AI-powered annotation tools to label your images and videos, here are some of the reasons:
- Increased Efficiency And Accuracy: As mentioned, AI-powered tools allow for process automation, which reduces human error. This leads to increased efficiency and accuracy in labeling and training your models.
- Real-Time Collaboration: These tools allow multiple users and processes to operate simultaneously, making it easier for users to share data and respond in real-time. They offer efficient and accurate ways to label and classify images and videos.
- Cost-Effective: Another reason to use AI tools is that they are cost and time-effective. They reduce the human resources needed for the project and the time and effort put into the annotation process, ultimately reducing the total cost.
- Higher Consistency: Using annotation tools ensures labeling consistency even when dealing with larger data sets, which is nearly impossible when dealing with manual annotation.
Generally, AI-powered tools allow you to improve your models’ accuracy and quality. You can start your image and video annotation journey with Superb labeling solutions for user-friendly annotation tools that simplifies the data process and assist in building quality data sets.
2. Integration With Other Technologies
Since many businesses use AI-powered annotation tools to label and classify videos and images faster, the demand for quality annotation services has increased. Companies from different industries, including healthcare, e-commerce, and autonomous vehicles, have integrated their annotation tools with other AI technologies to rise to the competition.
For instance, you can integrate annotation tools with other AI technologies to enhance your customers’ experience if you’re operating an e-commerce business. It helps you label your products appropriately using the right keywords, drawing more traffic to your site and generating more leads.
3. Increased Use Of 3D Images And Videos
Another trend to watch out for is the increased use of videos and 3D images. Businesses are accelerating their growth using AI data tagging and 3D images and video annotation to factor in their volume, distance, and depth perception. The best part is that you can use it to annotate your videos and images regardless of your market niche.
3D annotation has better visual insights and valuable data as it considers more dimensions. The results are more accurate and reliable, although it requires more time and advanced tools. However, you should note that it may be challenging to annotate in 3D compared to 2D.
The industry that benefits the most from 3D image and video annotation is autonomous vehicles. The 3D annotation allows these self-driving cars to understand how the real world operates. It’ll enable them to sense the volume and distance of other vehicles, pedestrians, and objects, avoiding damaging accidents.
4. Higher Focus On Data Accuracy And Security
The main contributing factor to using image and annotation tools is the increased focus on data accuracy, safety, and security. Many manufacturing and logistics firms are looking to incorporate blockchain technology to improve data security, transparency, and accountability in the supply chain.
Integrating image and video annotation with your blockchain technology allows you to record real-time changes that impact your delivery schedules automatically. You can then go back to the drawing table to create and implement new delivery strategies to reduce delivery time and errors, and streamline the process, based on the recorded changes.
Conclusion
Video and image annotation is crucial for training AI models. Businesses in various industries, including healthcare facilities, logistics firms, and automotive, are thriving off the benefits of image and video annotation, which you’d also want to take advantage of. Watch out for the discussed trends to ensure you keep up with the changing world and create better and more sophisticated models.
