Video Data Collection Services for AI Training
We capture, annotate, and deliver video datasets engineered for the demands of computer vision, action recognition, and multimodal AI. From single-camera action sequences to multi-sensor synchronized footage — indoors, outdoors, or in controlled studio conditions — we produce video training data that is temporally precise, richly annotated, and ready for your pipeline.
- 25+ crowdsourcing platforms
- 30+ industries
Our Expertise
Footage of people performing everyday actions, occupational tasks, physical activities, and social interactions, recorded across diverse demographics and environments.
Industry use cases
- action recognition models
- fitness AI
- surveillance systems
- healthcare monitoring
- sports analytics.
Industry use cases
- emotion recognition
- sign language translation
- avatar animation
- lie detection
- AR/VR interaction
Industry use cases
- autonomous vehicle perception
- traffic flow analysis
- road safety systems
- insurance telematics.
Industry use cases
- retail analytics
- footfall counting
- loss prevention AI
- smart city infrastructure
Industry use cases
- quality control vision systems
- PPE compliance detection
- robotic process automation
- worker safety monitoring
Industry use cases
- autonomous driving simulation
- rare event training
- edge case coverage
- domain randomization.
Video Data Collection Methods
Platforms and Tools
Capture hardware
Multi-camera rigs (RGB, depth, IR), GoPro and body-worn cameras, dashcam arrays, drone-mounted cameras, custom IoT capture kits
Annotation tools
CVAT, Labelbox Video, Scale AI, SuperAnnotate; proprietary temporal annotation interface for frame-level and track-level labels
Synthetic generation
CARLA, Unity Perception, NVIDIA Omniverse, Blender scripted pipelines
Processing
FFmpeg, OpenCV, DeepStream; custom frame extraction and synchronization tooling
Formats
MP4, MOV, AVI source; JSON, COCO, YOLO, and custom schema for annotation delivery; per-frame or track-level label options
Project Steps
01
Discovery & requirements scoping
We define scenario coverage, camera setup, frame rate, resolution, annotation type (bounding box, segmentation, keypoint, track), demographic requirements, and volume targets.
02
Scene & collection design
Our team designs the capture environment, recruits participants or deploys camera infrastructure, and produces detailed scenario scripts and annotation guidelines.
03
Pilot capture & annotation review
A pilot batch is filmed, annotated, and reviewed against your quality benchmarks. Annotation label accuracy, temporal consistency, and coverage gaps are assessed before scaling.
04
Full-scale collection & annotation
Production recording runs across all planned scenarios and locations. Frame-level and track-level annotation runs in parallel with automated pre-labeling to accelerate throughput.
05
Quality assurance
Automated checks (frame drop detection, resolution validation, label consistency) combined with human expert review ensure spatial and temporal annotation accuracy across all deliverables.
06
Delivery & iteration
Final datasets are delivered in your target format via secure cloud transfer. We support ongoing collection for new scenarios, model-identified edge cases, and incremental dataset expansion.
Frequently Asked Questions
Do you provide video data validation and verification?
Yes. Validation criteria are derived from the technical specification and can cover completeness, file integrity, video format and metadata, technical quality, annotation consistency, and scenario compliance across the collected footage. Checks run during the collection process and again before delivery, with acceptance thresholds and rework rules agreed in advance.
How do you manage consent, privacy, and data usage rights?
Before launch, we define the lawful basis or source permission, participant notices and consent where required, permitted uses, retention period, and transfer conditions for video clips featuring identifiable people. Personal data, including anything tied to facial recognition or face recognition use cases, is minimized and can be pseudonymized or anonymized where appropriate, with exact controls depending on jurisdiction and the client’s intended use.
What data and metadata formats are supported for delivery?
We support standard and project-specific video formats needed for deep learning pipelines. Before production, we agree on the file type or codec, folder structure, naming convention, identifiers, timestamps, metadata schema, and delivery method so annotated datasets integrate into the client's ML models.
Can video be collected in multiple languages and regions?
Yes. We support multi-region video collection, subject to participant availability and local legal and operational constraints. The specification can define geography, demographic quotas, environment conditions, and reviewer qualifications, with domain-qualified reviewers involved where language elements such as video transcription are required.
How do you ensure video data quality during collection?
Quality is controlled at three stages: before launch, during collection, and before delivery. We validate the specification and pilot, monitor recording parameters during the collection process, and run automated checks alongside human review against agreed thresholds for detecting objects, tracking human movements, and identifying objects accurately.
Ready to get started?
Tell us what you need — we’ll reply within 24h with a free estimate
We partnered with Unidata for a vast collection of webcam and smartphone assets and were very satisfied. The data quality was excellent, maintaining high resolution and clarity across all formats to meet our specific technical needs. The procurement process was seamless thanks to their customer service. The team was proactive and transparent, ensuring smooth communication from start to finish. Although the investment was significant, the professional caliber of the data and the reliability of the service made it a worthwhile value.
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