AI Video Data Collection

Training data that teaches AI to see motion

End-to-end video data collection and frame-accurate annotation — from controlled action capture campaigns to multi-object tracking and temporal activity segmentation — built for computer vision, robotics, and autonomous systems.

● Multi-object tracking ● 3 tracks active

ID:003 • walking

ID:001 • forklift

ID:002 • carrying

FRAME 0847 / 3200 3 OBJECTS TRACKED QA ✓ 98.9%

⏮ ▶ ⏭ 00:28:06 / 01:46:40

Trusted by AI teams worldwide

10K+

Hours annotated

98.5%

QA accuracy

40+

Action Categories

2K+

Domain expert annotators

48h

Pilot batch turnaround

Use cases

Video datasets for every motion AI application

Whether you’re training an action recognizer, a tracking system, or an autonomous driving model — Synnth sources and labels the video data your model needs to understand the physical world.

Action & Activity Recognition

Diverse clip-level and frame-level action labels across human activities — sports, workplace tasks, daily living, and safety-critical behaviors — with fine-grained temporal boundaries.

Kinetics-style AVA format Temporal bounds Multi-label

Multi-Object Tracking (MOT)

Consistent object identity tracking across frames — vehicles, pedestrians, animals, and products — with occlusion handling, re-identification, and trajectory metadata for tracking model training.

Bounding box tracks Re-ID labels Occlusion flags MOT format

Autonomous Driving & ADAS

Dense multi-class video annotation for self-driving systems — vehicle and pedestrian tracks, lane markings, traffic signs, drivable area segmentation, and event detection across weather and lighting conditions.

Lane tracking Event detection Night/rain/fog Dashcam data

Warehouse & Industrial Robotics

Worker activity monitoring, forklift and conveyor tracking, picking and packing action labels, and safety event detection for warehouse automation and human-robot collaboration AI.

Worker actions Equipment tracks Safety events Overhead CCTV

Healthcare & Clinical Video

Clinical action recognition, surgical phase detection, patient monitoring activity labels, and rehabilitation exercise classification — under HIPAA-compliant collection and annotation protocols.

HIPAA-ready Surgical phases Rehab exercises Gait analysis

Security, Surveillance & Anomaly

Crowd counting, person re-identification, loitering detection, fight recognition, and abandoned object labeling for intelligent video surveillance and public safety AI systems.

Crowd density Re-ID labels Anomaly events Multi-camera

Annotation types

Every video labeling method, done precisely

Frame-accurate annotation with temporal consistency validation at every stage.
Our annotators are trained per task type with strict QA rubrics.

Bounding Box Tracking

Frame-by-frame object bounding boxes with persistent IDs across the full clip. Keyframe annotation with interpolation and manual correction.

Temporal Segmentation

Start and end frame boundaries for activity segments — action clips, event windows, and phase detection with multi-track support.

Pose & Keypoint Tracking

Skeleton joint tracking across video frames for action recognition, gait analysis, sports science, and ergonomics monitoring in video.

Video Segmentation

Per-frame semantic and instance segmentation masks with temporal propagation — for scene understanding, autonomous driving, and background separation.

Event & Anomaly Detection

Precise timestamp marking for events, incidents, and anomalies — falls, near-misses, traffic violations, equipment failures — with severity and context metadata.

Video Classification

Clip-level and scene-level category labels for action recognition datasets — single-label, multi-label, and hierarchical taxonomies at scale.

Multi-Camera & Re-ID

Consistent identity labels across multiple camera views for cross-camera person re-identification, wide-area tracking, and multi-view action recognition.

Depth & 3D Video

Depth map annotation, 3D bounding box tracking, and point cloud labeling for RGB-D video — used in robotics, scene reconstruction, and spatial AI.

What we collect & annotate

From raw footage to production-ready video dataset

Synnth manages the complete video data pipeline — controlled shoot coordination, footage sourcing, frame annotation, temporal labeling, and QA delivery.

Video collection

Controlled action capture campaigns - directing consented participants through scripted activity sequences.
Environment-specific shoots - warehouses, hospitals, retail stores, outdoor, in-vehicle.
Multi-camera rig coordination - synchronized multi-view shoots for 3D and re-ID datasets.
Edge-case & adversarial scenarios - occlusion, low light, partial view, crowd conditions.
Licensed footage sourcing - sports, CCTV-style, dashcam, drone, and domain-specific video licensing.
Synthetic video augmentation - rendered video for rare events, dangerous scenarios, long-tail classes.
Demographic diversity control - balanced age, gender, ethnicity, body type in action datasets.

Annotation & labeling

Bounding box tracking - frame-level object detection with persistent IDs and interpolation.
Temporal activity segmentation - start/end boundaries for action clips and event windows.
Pose & skeleton tracking - keypoint tracking for human and animal pose in video.
Semantic & instance video segmentation - per-frame mask propagation with consistency checks.
Event & anomaly timestamping - incident marking with severity, context, and cause metadata.
Video classification - clip-level and scene-level category labels at scale.
Attribute labeling - gender, age, clothing, speed, direction, interaction type per track.

How it works

From brief to production-ready video dataset

A transparent four-stage pipeline — designed for computer vision teams who need frame-accurate, temporally consistent data at scale.

Define scope

Share your use case, action taxonomy, environment requirements, camera conditions, and annotation schema. We co-design ontologies, edge-case guidelines, and QA rubrics with your CV team.

Source & capture

We coordinate controlled video capture sessions with consented participants, or source existing licensed footage matching your domain. Diversity quotas and quality standards are enforced before annotation.

Annotate & QA

Expert annotators label your video using frame-accurate tooling. Every clip passes temporal consistency checks, per-track validation, and senior reviewer sign-off. QA report included with every delivery.

Deliver & iterate

Receive clean datasets in COCO Video, MOT, AVA, Kinetics-style JSON, or custom formats. Ongoing batch delivery on your schedule, with the same QA standards and annotator pool every time.

Why Synnth

Built for teams who can't afford drift

Six things that separate Synnth from generic video labeling platforms — especially for temporal consistency, which is where most video annotation fails.

Temporal consistency QA

Object identities, mask boundaries, and keypoints are validated not just per frame but across the full temporal span of each clip. Drift and ID switches are caught by automated consistency checks before human review.

Frame-accurate

Domain-expert annotators

Healthcare video annotated with clinical knowledge. Automotive data by engineers familiar with driving scenarios. Industrial video by professionals who recognize workplace activities and safety events.

200+ specialists

Controlled capture campaigns

We don’t just label your existing footage. We design and run controlled video collection campaigns — directing participants through specific activity sequences to fill exact data gaps in your training set.

Enterprise-grade security

All video encrypted at rest and in transit. GDPR compliant, HIPAA-ready for healthcare video. NDAs on every engagement. Footage of participants handled under strict consent and data protection protocols.

Fast pilot SLAs

Pilot batches of up to 10 hours of annotated video delivered within 48–72 hours — so you can validate annotation quality and temporal accuracy before committing to full production volume.

48h pilot

Fast pilot SLAs

We build task-specific action ontologies, edge-case handling guides, and inter-annotator calibration sessions — designed around your model’s deployment domain and the specific edge cases that matter to your accuracy metrics.

Zero generic rubrics

Industries

Video annotation expertise across every sector

Annotation teams matched to your industry’s terminology, regulatory environment, and quality standards — not generic workflows applied uniformly.

Autonomous Vehicles

Dashcam and roadside video annotation — vehicle and pedestrian tracking, lane events, traffic sign recognition, and near-miss detection across weather and geography.

Industrial & Warehouse

Worker activity recognition, forklift tracking, conveyor monitoring, picking/packing actions, and safety event detection for automation and workforce analytics.

Healthcare & Clinical

Surgical phase detection, patient activity monitoring, rehabilitation exercise classification, and fall detection under HIPAA-compliant collection protocols.

Sports & Fitness

Athlete pose tracking, action recognition across sports disciplines, form analysis, training drill classification, and team movement pattern labeling.

Retail & Smart Stores

Shopper journey analysis, shelf interaction tracking, queue management events, and product pick-and-place activity labeling for retail AI and loss prevention.

Security & Public Safety

Crowd density estimation, loitering detection, fight recognition, perimeter breach labeling, and multi-camera person re-identification for surveillance AI.

Output formats

Delivered in the format your pipeline expects

No conversion scripts. Video annotations arrive clean and structured, ready for ingestion into your training infrastructure.

COCO Video JSON MOT CSV AVA JSON Kinetics-style JSON CVAT XML DAVIS JSON YOLO Video TXT ActivityNet JSON THUMOS CSV Waymo TFRecord nuScenes JSON Frame-extracted PNG + JSON Custom schema

FAQ

Common questions about AI video data collection

Everything you need to know before starting a video annotation project with Synnth.

💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.

What is AI video data collection?

AI video data collection is the process of sourcing, capturing, and curating video footage specifically to train computer vision models for tasks such as action recognition, multi-object tracking, activity detection, temporal segmentation, and video classification. The temporal dimension — how actions and objects change across frames — makes video annotation significantly more complex than image annotation.

How does frame-accurate video annotation work?

Frame-accurate annotation involves labeling objects, actions, or attributes at the individual frame level throughout a video clip. Annotators begin by placing labels at keyframes, then use interpolation between keyframes for efficiency. Every frame where motion, occlusion, or scene changes occur requires manual inspection and correction. Synnth validates temporal consistency across the full clip — checking that object IDs don’t switch, boundaries remain accurate, and action labels align precisely with their visual evidence in the footage.

What is the difference between action recognition and activity detection data?

Action recognition datasets classify what is happening in a pre-trimmed clip (e.g., “this 10-second clip shows running”). Activity detection datasets require both locating when actions occur within an untrimmed video (temporal start/end boundaries) and classifying what those actions are. Both are supported by Synnth — activity detection is more complex and requires more precise temporal boundary annotation.

What video formats does Synnth accept for annotation?

We accept most common video formats — MP4 (H.264, H.265), MOV, AVI, MKV, WebM, and raw frame sequences (JPG/PNG). For very high-resolution or RAW camera formats, please contact us during scoping to confirm compatibility. Annotations are delivered as JSON, CSV, or XML alongside frame-extracted images where required by your pipeline.

How does Synnth handle occlusion in multi-object tracking?

When a tracked object is occluded (partially or fully hidden by another object or by going out of frame), Synnth annotators flag the frames as occluded in the metadata, maintain the object’s persistent ID so identity continuity is preserved, and re-associate the correct ID when the object reappears. Occlusion handling quality is one of the primary QA dimensions we measure for tracking tasks.

Can Synnth run controlled video capture campaigns?

Yes — this is one of Synnth’s core capabilities. We design and run controlled video shoots with consented participants performing specific activities to your brief. This is especially valuable when you need rare actions, specific environments (warehouses, clinics, vehicles), demographic balance, or adversarial conditions (low light, partial occlusion, crowded scenes) that are hard to source from existing footage.

What is the turnaround time for video annotation projects?

Pilot batches of up to 10 hours of annotated video can typically be delivered within 48–72 hours. Video annotation velocity depends on annotation task complexity — tracking and segmentation take longer per hour of footage than classification. Enterprise projects are scoped with custom SLAs and a dedicated project manager. We share velocity estimates in our initial scoping response.

How is participant consent handled in video collection campaigns?

All participants in Synnth video collection campaigns sign informed consent forms before any footage is recorded. Consent documents specify how the footage will be used (AI model training), whether it will be shared, and the participant’s rights. For sensitive environments like healthcare or with minors, we apply additional consent protocols. All participant data is handled under GDPR-compliant data processing agreements.

What output formats are video annotations delivered in?

We deliver in your preferred format: COCO Video JSON, MOT CSV, AVA JSON, Kinetics-style JSON, CVAT XML, ActivityNet JSON, Waymo TFRecord, nuScenes JSON, YOLO Video TXT, and custom schemas. Output format is agreed during the scoping phase at no additional cost for standard formats.

Get started

Start your video data project today

Tell us your use case, action taxonomy, environment requirements, and volume targets. Our team responds within one business day with a scoping plan and no-obligation quote.