Video Annotation & Labeling Services for Computer Vision
Frame-accurate annotation across bounding boxes, polygons, segmentation and 3D cuboids — delivered by expert trained annotators with SOC 2, HIPAA and GDPR-ready workflows.
Why is Video Annotation?
Video annotation is the process of labeling objects, actions, and events across video frames to create training data for computer vision models. It enables AI systems — including autonomous vehicles, surgical-imaging models, retail-analytics platforms and robotics — to detect, track and classify moving objects in real-world footage. Shaip delivers frame-accurate video annotation across nine techniques, including bounding boxes, polygon segmentation, 3D cuboids and skeletal keypoints.
Imagine training the knowledge database of a self-driving car before unveiling the prototype. To be able to function at top capacity, the autonomous vehicle should be able to identify signals, people, roadblocks, barricades, etc., to drive through with accuracy and precision. However, this can only be made possible if machine learning & computer vision models can learn using the labeled data sets, used to train the algorithms.
Our Expertise
Productive Video Labeling Made Easy
Capture each object in the video, frame-by-frame, and annotate it to make the moving objects recognizable by machines with our advanced video labeling services. We have the technology and the experience to offer video labeling solutions that help you with comprehensively labeled datasets for all your video labeling needs. We help you build your computer vision models accurately and with the desired level of accuracy. Define your use case and let Shaip do the heavy lifting of powering vision models, with the following tools at our disposal:
Bounding Box Annotation
The most widely used video labeling technique. We draw 2D rectangles around target objects across every frame, enabling object-detection and tracking models for autonomous driving, surveillance and retail-analytics use cases.
Polygon Annotation
For irregularly shaped objects where bounding boxes overstate object area. Our annotators trace exact object boundaries frame-by-frame — essential for accurate scene parsing in medical imaging and aerial / drone footage.
Semantic Segmentation
Pixel-level classification of every region in every frame. Used where models need to distinguish road from sidewalk, tumor from healthy tissue, or product from background — anywhere pixel-accurate context matters.
Keypoint Annotation
Frame-by-frame landmark labeling for face, body and object points of interest. Powers facial-recognition, gesture-detection, biometric security and emotion-analysis models.
3D Cuboid Annotation
Three-dimensional bounding volumes for objects in space — vehicles, pedestrians, equipment. The standard annotation type for autonomous driving perception stacks and warehouse robotics.
Line & Polyline Annotation
Linear-feature labeling for roads, lanes, railways, pipelines and boundaries. Critical training data for lane-detection, infrastructure-inspection and autonomous-navigation models.
Frame & Video Classification
Classify entire frames or video segments by scene, action, or event type. Used for content-moderation, sports-analytics and media-archive indexing models.
Video Transcription
Audio-to-text transcription synchronised to frames, plus paired text + visual annotation for multimodal AI and generative-AI training.
Skeletal & Pose Annotation
Body-keypoint and skeletal-rig labeling for posture, motion and activity analysis. Powers sports-performance models, physical-therapy applications and humanoid-robotics training datasets.
Video Annotation Use Cases
Shaip provides effective video annotation solutions for a variety of applications.
In Cabin Driver Monitoring
Annotated hundreds of hours of driver and in-car video footage. Each video contains thoroughly annotated clips featuring facial feature movement, and in-car scenarios to accurately monitor driver behavior and give warnings when deviations are observed.
Retail AI
Video annotation is also helpful in retail stores to understand consumer behavior. With our annotated videos, it is easy to design applications to track shopper movement, understand buying decisions, and identify theft.
Traffic Surveillance
Video annotation has a significant role to play in developing high-quality surveillance applications. We have successfully annotated hundreds of hours of surveillance and CCTV videos at a superior level of resolution and detailing by annotating required objects.
Facial Recognition
Shaip is capable of applying key points on the face of a person to be used in developing high-end training datasets for developing facial recognition applications.
Lane Detection
Advanced capabilities in video annotation allow us to sift through hours of videos and use Polyline annotation to train vehicles to detect lanes, road markings, vehicular traffic, diversions, street lanes, and directions.
Computer Vision & Robotics
By training perceptive robots on using, adapting, and responding to their environment without the need for human interaction, it is possible to reduce fatalities and accidents that boost productivity.
Multi-Label Annotation
For certain labeled categories, you need to fixate on sub-categories to taper down decision-making and make analysis even more accurate. Instance annotation, as a part of multi-label video annotation, helps you with the same by categorizing vehicles further as buses, cars, and more.
Video Data Analysis
In case you want to analyze the video labeling need before planning a full-fledged training strategy, you can always rely on our video data analysis that aims at helping you plan the use cases better, plan out highly specific goals, and eventually allow us to deploy the right annotation technique.
Custom Annotation
Once the video data analysis is over, we can even help you plan out custom annotation strategies supported by the right video annotation tool, even if your use case is highly elusive and requires further detailing.
Video Labeling – Human Touch for Your AI
Long story short — Shaip lets you access some of the most advanced video annotation solutions to ideate perceptive and highly intelligent models. As a video annotation company, Shaip lends the most effective model training firepower to your goal-specific setups, fortified further with data mining tools, in-house data labeling teams, and the ability to bring in a wide range of video annotation tools to suit every relevant use-case.
If you outsource video labeling requirements to Shaip, you can get your hands on the following resources:
- Ability to handle longer videos and extract info
- Automated annotation perspective for faster time-to-market
- Access to frame-by-frame labeling
- Industry-specific coverage
- Higher accuracy
- Ability to process insane volumes of data
Why teams choose Shaip for video annotation
Dedicated pods, not anonymous crowds
Your project is staffed with a fixed, trained annotator pod plus a dedicated project manager, solutions engineer and QA lead — no rotating crowdworkers. Quality stays consistent across batches.
Trained annotators across the network
A global annotator workforce of 30,000+ specialists across data creation, labeling and QA — letting us scale a project from a 100-hour pilot to a 100,000-hour delivery without changing partners.
Multi-tier QA on every batch
Every delivery passes through annotator-level checks, peer review, project-manager QA and statistical sampling — backed by Six-Sigma trained quality leads — so accuracy stays above 98% on production batches.
Compliance-ready from day one
SOC 2 Type II controls, HIPAA-aligned workflows for medical data, GDPR + DPDP-compliant data handling, NDAs across every annotator and ISO 27001 information-security practices.
Industries We Serve
As one of the industry-leading solutions providers, we help a variety of industries design and develop automation tools and models based on our suite of video annotation services. We bring together the capability of technology and the competence of human experts to analyze large data volumes to enhance production, reduce errors, and increase efficiency.
Autonomous Vehicles
Frame-accurate labeling of pedestrians, vehicles, lane markings, signage and road geometry for ADAS and full-stack AV perception teams. Hundreds of hours of in-cabin driver-monitoring and on-road footage delivered.
Healthcare & Medical Imaging
Annotation of surgical video, ultrasound sequences, endoscopy footage and behavioural-tracking video for clinical AI — under HIPAA-compliant, NDA-bound workflows.
Robotics & Physical AI
Egocentric and exocentric video annotation for humanoid robots, warehouse automation and embodied-AI training data. Multi-view, multi-frame labeling synchronised to pose data.
Surveillance & Public Safety
CCTV, drone-feed and body-cam annotation for threat-detection, crowd-analysis and forensic video models. High-resolution, high-throughput pipelines.
Retail & eCommerce
Shopper-flow tracking, shelf-monitoring, queue-length detection and loss-prevention labeling for retail-analytics and autonomous-checkout models.
Insurance & Claims Processing
Damage assessment, accident-reconstruction and claims-evidence labeling for insurance AI workflows. Captures
Services Offered
Expert image data collection isn’t all-hands-on-deck for comprehensive AI setups. At Shaip, you can even consider the following services to make models way more widespread than usual:
Text Annotation Services
We specialize in making textual data training ready by annotating exhaustive datasets, using entity annotation, text classification, sentiment annotation, and other relevant tools.
Image Annotation Services
We take pride in labeling, segmented image datasets to train discerning computer vision models. Some of the relevant techniques include boundary recognition & image classification.
Audio Annotation Services
Labeling audio sources, speech, and voice-specific datasets via relevant tools like speech recognition, speaker diarization, emotion recognition, is something we specialize in.
Recommended Resources
Offerings
First-Rate Video Data Collection to Train AI Models
We help you capture each object in a video frame-by-frame, we then take the object in motion, label it, and make it recognizable by machines. Collecting quality video datasets to train your ML models has always been a stringent and time-consuming process, diversity and the massive quantities required add to further complexity.
Buyer’s Guide
Buyer’s Guide for Video Annotation and Labeling
It is a fairly common saying we’ve all heard. that a picture could say a thousand words, just imagine what a video could be saying? A million things, perhaps. None of the ground-breaking applications we’ve been promised, such as driverless cars or intelligent retail check-outs, is possible without video annotation.
Solutions
Computer Vision Services & Solutions
Computer vision is an area of Artificial Intelligence technologies that train machines to see, understand, and interpret the visual world, the way humans do. It helps in developing the machine learning models to accurately understand, identify, and classify objects in an image or a video – at a much larger scale & speed.
Featured Clients
Empowering teams to build world-leading AI products.
Expert Assistance is just a click away. Plan on taking vision AI capabilities to the next level! Reach out to us.
Frequently Asked Questions (FAQ)
1. What is video annotation, and why does it matter for AI?
2. What is the difference between video annotation and image annotation?
3. How does Shaip ensure annotation accuracy?
Every batch passes a four-tier QA process: annotator self-check, peer review, project-manager statistical sampling, and Six-Sigma quality-lead audit. Acceptance thresholds and edge-case rules are locked during calibration before any production work starts. Production deliveries typically meet 98%+ accuracy against client gold-standard sets, with iteration loops built into every engagement.
4. How much does video annotation cost?
Video annotation pricing depends on annotation type (bounding box vs polygon vs segmentation), frame density, object count per frame, accuracy requirements and total volume. Per-hour and per-asset pricing are both available. Shaip’s pricing scales down significantly past the pilot stage; bounded-scope quotes are typically returned within 48 hours of receiving a sample dataset.
5. What types of video annotation does Shaip provide?
We deliver nine techniques: bounding box, polygon, semantic segmentation, keypoint, 3D cuboid, line and polyline, frame classification, skeletal / pose, and video transcription. Project teams typically combine two or three of these depending on the model architecture and use case — for example, autonomous-driving projects usually pair 2D bounding boxes with 3D cuboids and lane polylines.
6. Why outsource video annotation instead of doing it in-house?
In-house annotation pulls senior ML engineers and data scientists away from model work. A 60-second video at 30 fps generates 1,800 frames to label, and a typical computer-vision training set contains hundreds of hours of such footage. Outsourcing to a specialised partner gives access to trained annotators, mature QA processes, scalable capacity and compliance posture — without diverting the core ML team.
7. How is Shaip different from other video annotation companies?
Three differences. First, dedicated annotator pods instead of anonymous crowdsourcing — the same trained team works your data from pilot through scale. Second, a four-tier QA process led by Six-Sigma trained quality leads. Third, compliance-ready from day one: SOC 2 Type II, ISO 27001, HIPAA-aligned workflows and GDPR-compliant data handling. Free pilots are available on request.
8. What are the challenges in annotating videos for computer vision?
Challenges include managing large datasets, ensuring annotation accuracy, handling complex scenes, and eliminating bias in data labeling.
9. How does video annotation improve facial recognition systems?
Video annotation labels facial features, expressions, and key points, enabling AI to accurately identify and analyze faces in real-time for applications like security and biometrics.
10. How do companies handle large-scale video annotation projects?
Companies like Shaip use scalable platforms, experienced teams, and automation tools to handle high volumes of video data efficiently and accurately.
11. What are the main use cases for video annotation in AI applications?
Key use cases include driver monitoring, traffic surveillance, retail behavior analysis, medical imaging, facial recognition, autonomous driving, and robotics.
12. How does Shaip support businesses with video annotation services?
Shaip delivers high-quality, scalable video annotation services tailored to specific industries. Their expertise ensures accurate, bias-free data to accelerate AI model training and development.
