En cliquant sur "Accepter ", vous acceptez que des cookies soient stockés sur votre appareil afin d'améliorer la navigation sur le site, d'analyser son utilisation et de contribuer à nos efforts de marketing. Consultez notre politique de confidentialité pour plus d'informations.

Video annotation

Turn your videos into strategic assets for your AI models. Our video annotation services combine technical expertise and rigorous processes to produce accurate datasets adapted to your needs.

Ask us for a quote
Image of an AI wave
Image illustrating video annotation... artist view with multiple videos about to be prepared for AI

🎯 Frame by frame

Precise annotations to the nearest image: object tracking, motion detection, multi-object tracking... for your AI models in mobility, health or sport.

Exploit my videos for AI

🛠️ Tools and expertise

We combine adapted tools (interpolation, linear interpolation, keyframes) and trained teams to ensure smooth and consistent annotation.

Optimize my video pipeline

🔄 Temporal coherence

Our teams ensure consistency between the frames and the quality of the annotated sequences, for efficient models in dynamic environments.

Improving AI video analytics

Annotation techniques

Satellite view of roads with cars. Each car is annotated with a bounding box

Bounding Boxes

The type annotation Bounding Box consists in precisely delineating the objects of interest in an image using rectangles, in order to allow a computer vision model to learn to detect or recognize them automatically.

⚙️ Process steps:

Definition of the annotation plane and the classes of objects to be located

Manual or semi-automated annotation by bounding boxes (images, videos, satellite views, etc.)

Cross validation and quality control (consistency of labels, overlaps, coverage rate...)

Export annotations to standard formats (COCO, YOLO, Pascal VOC...)

🧪 Practical applications:

Industrial inspection — Detection of defects on parts in production

Autonomous driving — Tracking vehicles, pedestrians, traffic signs

Satellite imagery — Location of buildings, agricultural or forest areas

Image of a road, in a 2d annotation interface. Road is annotated with a complex polygon

Polygons

The annotation by polygons allows you to precisely delineate the complex contours of objects in an image (irregular shapes, nested objects, etc.), essential for models of instance segmentation or semantics.

⚙️ Process steps:

Definition of categories and segmentation criteria

Manually annotate objects by drawing polygons point by point

Quality control and cross-checking of contours and classes

Export in adapted formats (COCO, Mask R-CNN, PNG masks...)

🧪 Practical applications:

Industrial inspection — Precise detection of faulty areas

Autonomous driving — Segmentation of roads, sidewalks, vehicles

Satellite imagery — Delimitation of crops, buildings or natural areas

Image of object tracking used on smart glasses, to detect objects such as pedestrians

Object Tracking

THEObject Tracking consists in following one or more objects of interest in a video sequence frame by frame, in order to model their Trajectory in time.

⚙️ Process steps:

Selection of objects to track (car, person, animal, product, etc.)

Manual or semi-automatic annotation of the position frame by frame (bounding box, polygon,...)

Consistent association of a unique identifier for each monitored object

Adjustment and interpolation of missing frames if necessary

🧪 Practical applications:

Autonomous driving — Pedestrian and vehicle tracking in an urban environment

Retail — Analysis of the customer journey in stores to study buying behaviors

Sport — Player tracking for modeling performances or creating statistics in real time

Image of a scene... someone running through various frames, annotated with a bounding box

Temporal classification

Assign global or contextual labels to continuous sequences of a video, by segmenting them according to coherent periods (e.g.: calm/activity/alert).

⚙️ Process steps:

Definition of the temporal categories to be annotated (states, situations, activity levels, etc.)

Annotating time ranges with a single label per segment

Review and check the consistency between the transitions

Export annotated segments with start/end + associated class (formats: JSON, CSV, XML...)

🧪 Practical applications:

Behavioral studies — Identifying the phases: sustained attention/distraction/fatigue

Circulation — Sequence classification: fluid/dense/blocked

Monitoring — Segmentation of periods: active/inactive/system error

Urban scene with multiple pedestrians annotated using skeletal keypoints for pose estimation and motion analysis in computer vision

Pose Estimation

Annotate the body positions (keypoints) frame by frame in a video sequence, in order to model the movements of one or more individuals over time.

⚙️ Process steps:

Definition of the keypoint skeleton (e.g.: 17 points — head, shoulders, elbows, knees...)

Annotation of key points on each frame or by keyframes with interpolation

Manual review and correction in case of occlusion or ambiguity

Export in specialized formats (COCO keypoints, structured JSON, CSV per frame)

🧪 Practical applications:

Sport — Study of the technical gesture (throwing, jumping, typing...) in video training

Oversight — Detection of suspicious attitudes or motor anomalies

Health/Rehabilitation — Analysis of posture and joint amplitudes

Annotation interface showing a vehicle tracked across multiple video frames using bounding boxes with interpolation for efficient labeling

Interpolation

Automatically generate missing annotations between several key frames (Keyframes) in a video. This technique is used for speed up manual annotation, while maintaining sufficient precision for training AI models. This method is applicable to various types of annotations: bounding boxes, polygons, keypoints, etc.

⚙️ Process steps:

Manual annotation of objects or points on key frames (all X frames)

Activation of automatic interpolation in the annotation tool (CVAT, Label Studio, Encord, etc.)

Verification of the interpolations generated: trajectories, shapes, coherence

Manual adjustment of frames where interpolation is incorrect

🧪 Practical applications:

Logistics robotics — Fluid animation of moving objects between two positions

Embedded videos — Seamless tracking of vehicles or pedestrians without annotating each frame

Multimedia production — Accelerated annotation of long sequences for segmentation or tracking

Use cases

Our expertise covers a wide range of AI use cases, regardless of the domain or the complexity of the data. Here are a few examples:

1/3

🛍️ In-store customer behavior (Retail analytics)

Video annotations captured in stores to track movements, interactions with departments and products. Data makes it possible to analyze the customer journey or to automate the generation of heatmaps.

📦 Dataset: Videos from surveillance cameras, with spatial annotations (bounding boxes, tracking ID) and categorization of actions (watch, take, rest...).

2/3

🎾 Analysis of sports gestures

Annotated videos to follow athletes' movements frame by frame (pose estimation, keypoints). The sequences are classified by type of gesture (typing, jumping, running) to train automatic detection or intelligent coaching models.

📦 Dataset: Videos in fixed or mobile shot, annotated in keypoints or bounding boxes, with movement tags and temporal metadata.

3/3

🚧 Monitoring of sensitive areas

Annotated video sequences to detect and follow people, vehicles or incidents in secure environments (warehouses, construction sites, infrastructures).

📦 Dataset: Multi-camera videos with multi-object annotations, identification of classes (personnel, intruders, vehicles) and synchronized trajectory tracking over time.

Image of someone at the automatic cashier, in a shop... to illustrate fraud detection in a retail context

Why choose Innovatiana ?

Our added value

Extensive technical expertise in data annotation

Specialized teams by sector of activity

Customized solutions according to your needs

Rigorous and documented quality process

State-of-the-art annotation technologies

Measurable results

Boost your model’s accuracy with quality data, for model training and custom fine-tuning

Reduced processing times

Optimizing annotation costs

Increased performance of AI systems

Demonstrable ROI on your projects

Customer engagement

Dedicated support throughout the project

Transparent and regular communication

Continuous adaptation to your needs

Personalized strategic support

Training and technical support

Compatible with
your stack

We use all the data annotation platforms of the market to adapt us to your needs and your most specific requests!

labelboxcvatencord
v7prodigyubiAI
roboflowImage illustrating Label Studio, an annotation platform

Secure data

We pay particular attention to data security and confidentiality. We assess the criticality of the data you want to entrust to us and deploy best information security practices to protect it.

No stack? No prob.

Regardless of your tools, your constraints or your starting point: our mission is to deliver a quality dataset. We choose, integrate or adapt the best annotation software solution to meet your challenges, without technological bias.

Feed your AI models with high-quality, expertly crafted training data!

👉 Ask us for a quote
En cliquant sur "Accepter ", vous acceptez que des cookies soient stockés sur votre appareil afin d'améliorer la navigation sur le site, d'analyser son utilisation et de contribuer à nos efforts de marketing. Consultez notre politique de confidentialité pour plus d'informations.