Comprehensive Video Annotation Services for AI & Machine Learning
Classifies and labels named entities (e.g., people, organizations, locations, dates) for use in knowledge graphs, legal text analysis, intelligent search engines, and document classification
Identifies the purpose behind text inputs (e.g., queries, commands, feedback), essential for chatbots, voice-based systems, and conversational AI platforms.
Categorizes text based on emotion or tone (positive, negative, neutral) for use in social media monitoring, product feedback systems, and customer sentiment analysis.
Links concepts to a knowledge base to enhance AI comprehension and disambiguate meaning (e.g., “Apple” the brand vs. fruit), improving natural language understanding (NLU).
Traces pronouns or phrases back to the entities they reference (e.g., “John went home. He was tired.”), minimizing contextual ambiguity in language modeling.
Assigns grammatical roles (noun, verb, adjective, etc.) to each word, enabling syntactic parsing, linguistic modeling, and deep NLP structure learning.
Links concepts to a knowledge base to enhance AI comprehension and disambiguate meaning (e.g., “Apple” the brand vs. fruit), improving natural language understanding (NLU).
Traces pronouns or phrases back to the entities they reference (e.g., “John went home. He was tired.”), minimizing contextual ambiguity in language modeling.
Healthcare
Automobile
Finance
Security and Surveillance
Robotics
Agriculture
Statswork’s video annotation team brought precision and speed to our autonomous driving dataset. Their ability to track multiple objects across thousands of frames with consistency was outstanding. We’ve seen a significant improvement in model accuracy since integrating their annotations.
We partnered with Statswork for medical video annotation, including surgical tool tracking and procedural step labelling. Their understanding of clinical workflows and rigorous QA gave us confidence to scale our AI model for real-time OR insights.
Statswork’s team was instrumental in annotating drone footage for crop health analysis. Their understanding of spatial annotations and ability to work with geotagged video helped us train highly accurate models for agri-intelligence.
From object tracking to crowd behaviour labelling, Statswork delivered exceptional video annotation for our smart surveillance platform. Their turnaround time, attention to detail, and clear communication set them apart from other providers.
AI & ML
Predective Analyses
Data Analyses
In the simplest terms, video annotation is the process of labelling video data to make it machine learning and AI friendly. This process involves tagging a video frame-by-frame to identify objects, actions, or events so that AI systems can learn time-based patterns. Video annotation is vital to train models for applications that include surveillance, autonomous driving, behaviour detection, and more.
An example of image annotation would be labelling a photo of a car instead of breaking down the journey of that car along a dotted red line. When annotating a video, it is not just the box around the car that needs to be drawn, but the trajectory and identification of the car across many frames. Because a video has continuity, it is much more complex because of influence variables that must be accounted for, like motion, light, occlusion, and the need for temporal accuracy. These continuously moving objects are labelled in both a span of time and an absolute user’s distance of space, which must remain relative to the user or AI engine being processed.
There are a lot of different video annotation types we can aid in executing. We’ll help you label videos with any mix of the following types of annotations:
We serve a wide range of industries, including:
We work with industry-leading annotation tools, for example but are not limited to:
Again, depending on application, we achieve quality control for every annotation via the following:
Definitely. We design scalable video annotation pipelines that are designed to operate under the challenges of long video lengths, high frame rates, and/or multiple objects per video. We are capable at both the frame level for annotation purposes and using the entire video stream directionally.
If your integration programs and requirements for your ML pipeline allow, annotated video will be delivered in JSON, XML, YOLO, COCO, formats or as videos with the metadata overlaid.
Yes! Statwork has strict data privacy protocols, NDAs, and security policies. Our infrastructure is privacy-by-design, but we also have the ability to offer “on-premise” deployment if there are any highly sensitive data requirements.
Absolutely! Statswork builds workflows based on your domain, your use case, your complexity, and your ML objectives. In fact, this may even mean customizing a workflow for annotating surgical instruments to tracking behaviours in traffic. We are a flexible and adaptable tools and teams to your project.
