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.
🎯 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.
🛠️ Tools and expertise
We combine adapted tools (interpolation, linear interpolation, keyframes) and trained teams to ensure smooth and consistent annotation.
🔄 Temporal coherence
Our teams ensure consistency between the frames and the quality of the annotated sequences, for efficient models in dynamic environments.
Annotation techniques
Bounding Boxes
In Computer Vision, drawing a Bounding Box consists in precisely delineating the objects of interest in an image using rectangles, in order to allow a model (e.g. YOLO) to learn to detect or recognize them automatically.
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...)
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
Polygons
Annotation with polygons is used to precisely delineate the complex contours of objects in an image (irregular shapes, nested objects, etc.), essential for models of instance segmentation or semantic segmentation.
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...)
Industrial inspection — Precise detection of faulty areas
Autonomous driving — Segmentation of roads, sidewalks, vehicles
Satellite imagery — Delimitation of crops, buildings or natural areas
Object Tracking
Object 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.
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
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
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).
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...)
Behavioral studies — Identifying the phases: sustained attention/distraction/fatigue
Circulation — Sequence classification: fluid/dense/blocked
Monitoring — Segmentation of periods: active/inactive/system error
Pose Estimation
Annotate body positions (keypoints) frame by frame in a video sequence, in order to model the movements of one or more individuals over time.
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)
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
Interpolation
Automatically generate missing annotations between several key frames (keyframes) in a video. This technique is used to 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.
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
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:
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!
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!
