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Data annotation for video surveillance and video analytics

We prepare camera video and images for Computer Vision training — people and vehicle detection, object tracking, events, zones and complex scenes. From a pilot to a production-ready dataset.

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Video surveillance data annotation for Computer Vision training

Annotation quality directly affects video analytics accuracy

Problem

  • missed people, vehicles and other objects;
  • false system alerts;
  • lost object identity between frames;
  • errors under occlusion, poor lighting and difficult angles;
  • unstable video analytics in real conditions.

Solution

  • one rule set for every camera and scene;
  • accurate object detection and frame-by-frame tracking;
  • stable object IDs over time;
  • annotation of hard and borderline cases;
  • a dataset shaped for the model and the production scenario.

What is data annotation for video surveillance?

Data annotation for video surveillance is the preparation of camera images and video streams for computer vision training. Annotators mark people, vehicles, objects, zones and events, and track how objects move across consecutive frames.

Unlike work with separate photos, surveillance requires the temporal link between frames. The same person or car must keep a correct ID while moving, when partly occluded, or when scale and viewpoint change.

These datasets have to reflect real camera conditions: low light, shadows, rain and snow, crowded frames, distant views, motion blur and partial occlusions.

The model then receives structured data for training automatic video analytics, security and monitoring systems.

Annotation types for video surveillance

People and object detection

Bounding boxes, polygons or masks for people, cars, bicycles, items and other objects in the frame.

Object tracking

Video Object Tracking that keeps a unique object ID across frames and follows its motion.

Zones and trajectories

Controlled areas, crossing lines, movement directions and object trajectories.

Events and actions

Entry and exit, falls, object appearance, stops, crowds and other specified events.

Object and scene classification

Extra attributes: vehicle type, object category, scene state or other project parameters.

Complex scenes

Occlusions, small objects, night video, unusual angles and dense flows of people and traffic.

Annotation examples

Different annotation types for computer vision tasks

ML Pipeline

Full data preparation cycle from raw data to model-ready output

1
Data
Collect and prepare source audio data.
Order data prep
2
Annotation
Annotation aligned with task requirements.
Order annotation
3
Quality Control
Multi-step consistency and QA checks.
Check quality
4
Dataset
Final dataset in required format.
Get dataset
5
Model Training
Ready for ML/AI production pipelines.

Quality control

How US-DATA keeps video annotation stable

For surveillance it is not enough to mark an object on a single frame. An error can appear during motion, occlusion or a change in shooting conditions and then affect the whole track.

Annotation follows shared guidelines, and results pass multi-step review. We check object position, consistency between frames, ID correctness and the rules for hard cases.

The process and review criteria are adapted to the task, camera type, object classes and the specific ML model.

01
Stable tracking
We check that object IDs and annotations stay consistent across frames.
02
Hard-scene review
Occlusions, small objects, night frames and unusual angles are reviewed separately.
03
One annotation logic
Every annotator follows the same rules for objects, zones, events and borderline cases.

Where surveillance data annotation is used

Security systemsAutomatic detection of people, vehicles and specified events.
Perimeter controlMonitoring object appearance and crossings of controlled zones.
Urban video analyticsPeople and traffic flows, Smart City and city infrastructure monitoring.
Parking and transportVehicle detection, motion analysis and entry/exit control.
Access controlAppearance and movement of people in controlled spaces.
Industrial safetyPeople, equipment, work zones and potentially dangerous situations.

US-DATA advantages

Computer Vision expertise

We understand how annotation quality affects detection, tracking and real video scenes.

Difficult video

Dense scenes, occlusions, small objects, varied angles and hard shooting conditions.

Scalability

Start with a test sample and scale to large image and video volumes.

Project flexibility

We follow your guidelines or help define rules for the model.

Stable quality

Multi-step QA and one standard for the whole dataset.

Result for your ML project

1

More accurate object detection

2

Stable tracking between frames

3

Fewer false alerts

4

Reliable work on complex video scenes

5

A dataset ready for training and production

Video data security

Enterprise-grade data protection
Security & Compliance
NDA before the project starts.
Compliance with the customer's local law and international standards.
In-house staff only, with no transfer of data to third parties.
Access control and permission boundaries.
Secure storage and transfer.

Pricing

Expandable sections with indicative cost tables.

Calculate annotation cost

Choose parameters and get instant estimate

Segmentation
Bounding Box
Tracking
Classification / events
1,000 data units

Our offer

Price per 1,000 units$150
Number of data units1,000
Number of classes1
ComplexityLow
Project cost$150*
Detailed termsCreate request

* This estimate is not a public offer. Final cost is determined after technical analysis and data review.

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Latest materials on data annotation and machine learning

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Need data annotation for a video surveillance system?

Send a request — we will estimate the volume, annotation requirements and the best approach for your ML task.

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Data annotation for video surveillance and video analytics

Data annotation for video surveillance is a key step in preparing datasets for computer vision and automatic video analytics. A neural network needs a large volume of carefully labeled images and video to detect people, vehicles and other objects, follow their movement and recognize events.

US-DATA annotates video data for surveillance, security and Computer Vision. We work with single frames and video sequences, perform object detection, bounding boxes, image segmentation, classification, frame-by-frame labeling and Video Object Tracking with stable IDs across frames.

Datasets for video analytics must reflect how cameras actually operate. People and vehicles overlap, sit far from the camera, appear in low light or move quickly. These cases need shared annotation rules and separate quality control, otherwise training errors turn into false alerts and unstable tracking.

US-DATA can join a pilot or a large-scale annotation program. Our own infrastructure, shared guidelines, multi-step review and specialists who understand ML and Computer Vision keep annotation consistent at volume.

If you need a surveillance dataset, video annotation for neural networks, people and vehicle detection or object tracking, US-DATA will prepare data in the agreed format for training, testing and production.