Video Annotation Services
Frame-accurate labels that teach AI to read motion
Expert human video annotation across multi-object tracking, action recognition, temporal activity segmentation, pose tracking, and event detection — with temporal consistency QA and 48h pilot delivery.
Trusted by AI teams worldwide
10K+
Hours annotated
98.5%
QA accuracy
40+
Action categories
2K+
Domain expert annotators
48h
Pilot batch turnaround
Annotation types
Every video labeling method, done frame-accurately
Temporal precision separates useful video annotation from noise. Each task type is handled by specialists trained on task-specific QA rubrics — with consistency validated across the full clip, not just frame-by-frame.
01
Bounding Box Tracking
Frame-by-frame object bounding boxes with persistent identity IDs maintained across the full video clip — including through occlusion, re-entry, and crowd scenes. Keyframe annotation with validated interpolation.
02
Temporal Activity Segmentation
Start and end frame boundary labeling for action clips and activity windows — multi-track support for simultaneous activities, event timestamps, and phase detection with per-segment metadata.
03
Action & Activity Recognition Labeling
Clip-level and frame-level action category labels across human activities — single-label, multi-label, and hierarchical taxonomies for action recognition model training and benchmarking.
04
Pose & Keypoint Tracking in Video
Skeleton joint keypoint tracking across video frames — maintaining pose consistency through motion, occlusion, and viewpoint change. For fitness AI, sports analytics, ergonomics monitoring, and clinical gait analysis.
05
Event & Anomaly Detection Labeling
Precise timestamp marking for incidents, safety events, and anomalies — falls, near-misses, traffic violations, equipment failures — with severity rating, cause classification, and contextual metadata.
06
Video Semantic & Instance Segmentation
Per-frame semantic segmentation masks with temporal propagation — each pixel classified consistently across frames for scene understanding, autonomous driving, and background/foreground separation at video scale.
Use cases
Video annotation for every motion AI application
From training action recognition models to building real-time safety monitoring systems — every video AI depends on precisely labeled temporal data. Synnth delivers it.
Autonomous Vehicles & ADAS
Dense multi-class video annotation — vehicle and pedestrian tracks, lane events, traffic sign sequences, and near-miss detection across weather, lighting conditions, and geographic regions.
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.
Sports & Fitness AI
Athlete pose tracking, action recognition across sports disciplines, form analysis, training drill classification, and team movement pattern labeling for sports analytics and fitness platforms.
Healthcare & Clinical Video
Surgical phase detection, patient activity monitoring, rehabilitation exercise classification, and clinical gait analysis — annotated by clinical professionals under HIPAA-ready protocols.
Security & Surveillance AI
Crowd density estimation, loitering detection, fight and anomaly recognition, person re-identification across cameras, and perimeter breach labeling for intelligent surveillance systems.
Retail & Smart Store Analytics
Shopper journey tracking, shelf interaction recognition, queue event labeling, and product pick-and-place activity annotation for retail AI, store analytics, and inventory automation.
Quality assurance
QA built for the demands of temporal consistency
Video annotation has a unique quality challenge that images don’t: identity drift, ID switches, and label inconsistency across frames. Our QA pipeline is built specifically to catch and prevent these failures.
Temporal consistency is the primary quality dimension in video annotation — an object’s ID, class label, and boundary must be accurate not just in a single frame but across every frame of its presence. Synnth validates consistency across the full clip, not just on a per-frame sample.
ID-switch detection is applied automatically after every annotation batch — flagging any frame where a tracked object’s identity has been incorrectly reassigned. This catches the most common failure mode in multi-object tracking annotation before it reaches your training pipeline.
Full-clip consistency validation
Automated temporal consistency checks run on every annotation track — verifying that object IDs, label categories, and bounding box continuity are maintained across the complete video, not just sampled frames.
Occlusion-aware annotation
Objects hidden by other objects or exiting frame are tracked with occlusion metadata. Re-identification when objects reappear is validated against the original track ID — preventing the most common source of tracking annotation errors.
Domain-matched annotators
Healthcare video annotated by clinicians who understand clinical activities. Automotive data by engineers familiar with driving scenarios. Each domain has its own annotator cohort and calibration program.
How it works
From footage to production-ready annotated video dataset
A four-stage pipeline with temporal consistency gates — designed for CV teams who need reliable, scalable video annotation delivery.
Define scope
Share your use case, action taxonomy, annotation type, domain, and quality requirements. We co-design ontologies, edge-case handling guides, and consistency rubrics with your CV team.
Prepare & calibrate
Video is pre-screened for quality, segmented into annotation-optimal clips, and assigned to domain-matched annotators who pass calibration tests before production begins.
Annotate & QA
Expert annotators label your video. Every clip passes automated temporal consistency checks, ID-switch detection, and senior reviewer sign-off before delivery.
Deliver & iterate
Receive clean datasets in COCO Video, MOT CSV, AVA JSON, or custom formats — with a full QA report including consistency scores and ID-switch rates. Same annotator pool every batch.
Why Synnth
Built for teams where temporal accuracy is non-negotiable
Six things that separate Synnth from generic video labeling platforms — especially for the temporal consistency demands of tracking and action recognition annotation.
Temporal consistency QA
Object identities, label categories, and mask boundaries are validated not just per frame but across the full temporal span of each clip. Drift and ID switches are caught by automated checks before human review.
Frame-accurate
Domain-expert annotators
Healthcare video annotated by clinicians. Automotive data by CV engineers familiar with driving scenarios. Industrial video by professionals who recognise workplace activities and safety events in context.
200+ specialists
Controlled capture campaigns
Beyond annotation — we also run controlled video capture sessions to fill data gaps in your training set with footage of specific activities, environments, and edge cases you can’t source from existing footage.
Custom action taxonomies
We build task-specific action ontologies, edge-case handling guides, and annotator calibration programs for your deployment domain — not generic rubrics that generate systematic errors on your corner cases.
Enterprise security
All video encrypted at rest and in transit. GDPR compliant, HIPAA-ready for clinical footage. NDAs on every engagement. Footage of participants handled under strict consent and data protection protocols.
Fast pilot SLAs
Validate annotation quality — consistency scores, ID-switch rates, action label accuracy — before committing to full production volume. Pilot batches of up to 10 hours in 48h at full QA standards.
48h pilot delivery
Input & output formats
Delivered in the format your pipeline already expects
No conversion scripts needed. Video annotations arrive structured and clean, ready for ingestion into your training infrastructure.
Industries
Video annotation expertise across every sector
Annotation teams matched to your industry’s domain vocabulary, regulatory requirements, and quality standards — not generic workflows applied uniformly across all video types.
Autonomous Vehicles
Dashcam and roadside video for self-driving — vehicle and pedestrian tracking, lane events, near-miss and traffic incident labeling across diverse geographies and conditions.
Industrial & Warehouse
Worker activity recognition, equipment tracking, picking/packing actions, conveyor monitoring, and safety event detection for warehouse automation and workforce analytics.
Healthcare & Clinical
Surgical phase detection, patient monitoring, rehabilitation exercise tracking, and clinical gait analysis under HIPAA-compliant protocols with medical professional annotators.
Sports & Fitness
Athlete pose tracking, action recognition, form scoring, training drill classification, and multi-player movement pattern labeling for sports analytics and coaching AI.
Retail & Smart Stores
Shopper journey tracking, shelf interaction labeling, queue monitoring, and pick-and-place action annotation for retail AI and loss prevention systems.
Security & Public Safety
Crowd density, loitering, fight detection, perimeter breach, and multi-camera person re-identification labeling for intelligent surveillance and public safety AI.
FAQ
Common questions about video annotation
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 annotation and how does it differ from image annotation?
AI video annotation is the process of labeling video footage frame-by-frame or at the clip level with structured metadata — object tracks, action labels, temporal boundaries, pose trajectories, or event timestamps. The key difference from image annotation is the temporal dimension: an object’s identity, class, and boundary must be consistent not just in a single frame but across every frame of its presence in the video. This temporal consistency requirement makes video annotation significantly more complex — and quality significantly harder to maintain — than single-image annotation.
How does Synnth maintain object identity across frames during tracking annotation?
Annotators assign a persistent ID to each object at its first appearance and maintain that ID through the full clip — including when the object is occluded, partially visible, or temporarily exits frame. Occlusion frames are flagged with metadata. Re-identification when objects reappear is cross-checked against the original track to prevent ID switches. Synnth applies automated ID-switch detection across every annotation batch before human QA review, achieving less than 0.5% ID-switch rate on standard tracking tasks.
What is the difference between action recognition and activity detection annotation?
Action recognition annotation classifies what is happening in a pre-trimmed clip — a fixed-length video segment is labeled with one or more action categories. Activity detection annotation goes further: the annotator must find when an action occurs within an untrimmed video (temporal start and end frame boundaries) and classify what that action is. Activity detection is more complex and time-intensive per hour of footage. Both are supported by Synnth, and many projects require both — clip labels for recognition model training and temporal boundaries for detection model training.
What video formats does Synnth accept?
Synnth accepts MP4 (H.264 and H.265), MOV, AVI, MKV, WebM, and raw frame sequences (JPG or PNG). For very high-resolution or RAW camera formats, we confirm compatibility during scoping. Annotations are delivered in your preferred format — COCO Video JSON, MOT CSV, AVA JSON, Kinetics-style JSON, CVAT XML, ActivityNet JSON, Waymo TFRecord, nuScenes JSON, or custom schemas.
How does Synnth handle occlusion in multi-object tracking annotation?
When a tracked object is fully or partially occluded, annotators flag the affected frames with an occlusion metadata attribute and maintain the object’s persistent ID across the occluded frames based on trajectory prediction. When the object reappears, the correct original ID is re-associated and validated against the prior track. Occlusion handling quality is a primary QA metric we track and report per delivery.
Can Synnth annotate clinical or surgical video under HIPAA-ready protocols?
Yes. Healthcare video annotation projects are staffed with annotators who have clinical knowledge relevant to the specific procedure or activity being labeled. All patient-identifiable footage is handled under HIPAA-ready data handling protocols — access-controlled annotation environments, full audit trails, NDAs, and Business Associate Agreements (BAAs) where required. Annotation work is performed only within secure, non-downloadable annotation environments.
What is the turnaround time for video annotation projects?
Pilot batches of up to 10 hours of annotated video are typically delivered within 48–72 hours at full QA standards. Annotation velocity per hour of footage depends on task complexity — bounding box tracking is faster per clip than pose tracking or semantic segmentation. For ongoing production runs, we scope velocity targets during the initial consultation and provide realistic estimates based on your specific task complexity — not optimistic projections.
How is proprietary video footage kept secure during annotation?
All video is uploaded through TLS-encrypted channels and stored with AES-256 encryption at rest. Annotation work is performed within access-controlled environments — annotators stream video through our secure platform and cannot download or export raw footage files. NDAs are signed on every engagement. For footage involving identifiable individuals, all data is handled under GDPR-compliant data processing agreements and explicit participant consent documentation.
Get started
Start your video annotation project today
Tell us your use case, action taxonomy, environment, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.
- info@synnth.com
- Mon–Fri, 9am–6pm IST
- Response within 1 business day
- No setup fees
- No setup fees
- NDA available on request
- Free pilot for qualifying projects