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Machine Learning and AI powered Video Annotation Services from NextWealth
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Video Annotation Services

Video annotation services involve breaking the video into individual frames and classifying these frames using various methods. Since the object of interest, is in motion, the task of labeling each element for a precise outcome can be difficult. Also, even a short video of 30secs can have a lot of images that need to be annotated and can be time-consuming. That is why a lot of organizations outsource such work to video annotation companies like NextWealth.

With NextWealth, companies can rapidly build a reliable data pipeline for their computer vision models. By combining our methodologies for video annotation services, we deliver high-quality data for accurate machine learning. We customize our video annotation services to cater to specific use case.

Types of Video Annotation Services that NextWealth offers

We make use of efficient and convenient video data annotation methods that can be easily adapted to any machine-learning models. Our video annotation services enable the detection of all objects of interest frame-by-frame and make them recognizable to AI models through appropriate classification.

Video Classification Service

We categorize certain events in the video to identify specific actions. Such classification can be applied to either describe the quality of the video or can be applied to the entire video.

Video Object Tracking Service

We annotate videos to track all the events of interest and localize them. Such object tracking using extrapolation of the frames around them allows machine-learning models to recognize the length, width, and height of the images easily.

Action Identification Service

Our video data annotation trains the machine learning models to identify objects in the real world. This is useful to track the motion of people in the real world by labelling all the events related to their actions.

Applications of Video Annotation Services

Medicine Industry

Our video annotation helps in tracking the movement of various cells in humans for early diagnosis. It also helps healthcare providers to deliver quality services using artificial intelligence and machine learning.

Sports AI

We annotate video to localize the human body poses and joints to achieve better action recognition. Analyzing such player movements helps in generating data sets to formulate strategies that can improve the game of players.

Driver Monitoring

Our video data annotation solutions can label and track the body movements in the video, making it easy for AI/ML models to interpret human behaviors. This can be used to give warnings if the driver is falling asleep to prevent accidents.

Security AI

Our video annotation solutions for security AI are useful for detecting human behaviour in CCTV footage. Skeletal annotations of this type can help the AI models to identify cases of threatening or unusual behaviours.


Video annotation makes the manufacturing industry more efficient by increasing the productivity of robots. As a result, these robots can help detect defective items in production or manufacturing errors.

Media and News Industry

We provide training datasets for better reporting and transcribing video interviews in the news and media industry. Such accurately labeled datasets can also result in the developing of AI models that automatically detect fake news.

Our Video Annotation Approach

At NextWealth we follow a solution-based approach that focuses on precision.

We are transparent in our service delivery insights and review each frame in the video. Our process of video annotation involves labelling relevant elements within each video, classifying them based on characteristics, and segmenting them using various methods to generate datasets that are easily recognizable.

Case Studies

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What is video annotation services in computer vision?

The video annotation process trains computer vision models to identify and detect objects in videos by labeling and classifying the images. As opposed to image annotation, video annotation involves annotating objects frame-by-frame so that machine learning models can recognize them. Machine learning functions can be optimized with such high-quality video annotation. The use of deep learning for video annotation is becoming increasingly prevalent across industries, such as self-driving cars, and medical AI.

What is the main purpose of video annotation?

The main purpose of annotating video is to make it easier for machine learning models that utilize AI algorithms to identify objects within the video. Unlike image annotation, this process of video data annotation involves annotating frame-by-frame objects in the video to make it recognizable for computer vision models.

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