Video Annotation Services for Computer Vision

Capture each object in the video with frame-by-frame annotated lines making the moving objects recognition for computer or machines.

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Video Annotation Services

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Key Features of Video Annotation Services

Utilize annotated video frames as training data to detect objects and track human poses


Use Annotated Video frames as Training Data for Accurate Results

We offer video annotation and labeling solutions for any type of video by using advanced techniques and tools. Our tools help you in building computer vision models that perform with optimum quality. We are well-equipped to offer top-notch annotated videos using best video annotation services for deep learning or machine learning.


Use Video Annotation to Track Objects in Self-Driving Vehicles

Video annotation helps autonomous vehicles identify other vehicles, street lights, sign boards, traffic signals, lanes, cyclists and pedestrians walking on the street. Through advanced video annotation tool, we annotate videos frame-by-frame assisting AI developers build a ground truth model that allows them to develop a fully functional and reliable autonomous vehicle.


Use Video Annotation to Estimate Poses and Track Human Activity

Annotating human poses enable machines to readily detect human activity and interactions in different scenarios. While understanding the computer vision problems, our experts can do live video annotation, use the most efficient tools and technique to accurately annotate the facial expressions of humans and how they pose while performing various actions.

Video Annotation Tools

The process involves labeling a video clip manually with descriptive tags like objects, people, or locations.

2D Bounding Boxes

2D Bounding Boxes

This tool involves drawing rectangular boxes over an object to indicate where it is located within a video frame. This tool can be used for identifying a person in a picture or categorizing an object.

3D Cuboid Annotation

This tool helps in identifying objects in a video frame and then drawing a bounding box over them to show their location within the frame. This technique entails clarification of images so that they can be converted from 2D into 3D to make them better accessible to AI models as per visual perception.

3D Cuboid Annotation
3D Point Cloud Annotation

3D Point Cloud Annotation

It’s used to analyze 3D scenes, detecting objects in 3D space, and identifying motion patterns in the video. It involves labeling and annotation of objects so that they can be used in AI projects including computer vision and machine learning.

Landmark Annotation

This technique is utilized for making the human face recognizable to machines. It assists in structuring computer vision in a manner that it adds value to "attention to detail".

Landmark Annotation
Lines & Splines

Lines & Splines

This involves spotting the starting and end points of lines, curvature of splines, or any other feature seen in the video. It tracks changes in direction, speed and shape over time.

Polygons Annotation

This involves drawing polygons around objects in an image or video, such as vehicles, people, and buildings. In a traditional setting, this kind of annotation was used for segmenting images.

Polygons Annotation
Events Classification

Events Classification

This is done manually and involves adding labels or tags or video clips relating to a particular event or class. It involves creating bounding boxes around objects in an image or video frame and labeling them.

Event Tracking

This is used for creating an event-tracking system offering valuable insights into user behavior like how frequently people visit a certain location etc.

Event Tracking

Video Annotation Use Cases

A wide range of AI use cases can be achieved using video annotation. We offer video annotation & labeling services to companies for various industries ranging from automobile to retail to e-commerce.


This involves accurately detecting objects including humans, vehicles, animals, and real-estate properties through AI.


Facilitating quick deployment of AI in vehicles for autonomous driving through well-annotated AI training data.


Using video annotation for automating processes for quick and accurate disease detection using AI and ML.


Training data integration with AI improves reporting quality, audio and video interview conversions.


High quality AI training data can be used for facilitating rapid workflow and accurate decision-making.


Implementing AI with annotated training data assists sportspersons and managers in analyzing and forging plan of action for boosting strength and enhancing performance.

Frequently Asked Questions

Video annotation is a process of identifying objects and events in content and then labeling those objects and events with the appropriate markup labels and video labeling tool. Machine learning algorithms can then use these labels to build models that can be used to extract meaningful information.

In case you are a startup, it is advisable to outsource your video annotation work. It helps in saving time, money and boosts accuracy. Doing it in-house will be time consuming.

Video annotation tool has features to help with accurate timestamp annotation, shape markup, recording of screen, frame-accurate feedback, and hassle-free sharing for better collaboration.

1. Dataset quality: The dataset quality is key for building any machine learning model. The curated dataset must be cleaned by removing inferior quality and duplicate data prior to annotation.

2. Use of right labels: Annotators need to have knowledge regarding the usage of dataset in training a machine learning model. If object detection is the main goal, then the data needs to be labeled using bounding boxes for getting the object coordinates.

3. Organizing the labels: Customized label structures, accurate labels and metadata are important for preventing objects from being classified incorrectly once the manual annotation work is done.

4. Using interpolation and keyframes: During video annotation, frames that contain objects moving in a predictable fashion can be identified and set aside. This will help in not labeling the entire video, but using them for interpolation and annotation. This helps in hastening the annotation process and maintaining quality.

5. User-friendly: Creating precise annotations that can be utilized later for training ML models require annotation tools that are robust. The choice of the right tool will make the process easy, economical and much more efficient.

Videos consist of several high-dimensional data making it tedious for computer vision to identify objects and events. Also, as the medium is complicated and dynamic, it needs human eyesight to gain a complete understanding.

Interested in Working with Us?

In today's tech-driven world, a career in Artificial Intelligence (AI) can be highly rewarding. Join our team of annotation specialists, and be a part of the company that creates high-quality training datasets.

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