Video Annotation Services for AI and Computer Vision
Train reliable computer vision models with frame-accurate, temporally consistent video annotations. Innovatiana delivers managed video annotation services for object detection, multi-object tracking, action recognition, pose estimation, segmentation and event classification.
🎯 Frame-Accurate Video Labeling
Create precise labels at frame, keyframe or sequence level for object tracking, motion analysis and multi-object tracking across mobility, healthcare, sports, retail and industrial applications.
🛠️ AI-Assisted Workflows, Human Quality Control
We combine fit-for-purpose video annotation tools, keyframe interpolation and trained human annotators to accelerate production while preserving label accuracy and consistency.
🔄 Consistent Tracking Across Frames
Our teams maintain stable object identities, class labels and trajectories across frames, including during occlusions, camera movement and changes in object appearance.
Video Annotation Techniques & Services
Video Bounding Box Annotation
Draw rectangular labels around objects in selected frames or throughout a video sequence to identify their class, position and size. Video bounding box annotation prepares training data for object detection, localization and tracking models such as YOLO-based architectures.
Definition of the annotation scope and the classes of objects to be located
Manual or semi-automated annotation by bounding boxes helps human annotators to annotate video frames faster (images, videos, satellite views, etc.)
Secondary review and quality control (consistency of labels, overlaps, coverage rate, ...)
Export annotations to standard file formats (COCO, YOLO, Pascal VOC...); export format must remain compatible with training pipelines
Industrial inspection — Detection of defects on parts in production
Autonomous driving — tracking and interpolation reduce the need to annotate every frame in a video sequence
Satellite imagery — Location of buildings, agricultural or forest areas
Polygon and Video Segmentation Annotation
Trace precise object boundaries across selected frames using polygons or segmentation masks. This method supports semantic and instance segmentation when rectangular boxes are not accurate enough for irregular, overlapping or deformable objects.
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...)
Road-scene understanding — Segment lanes, sidewalks, vehicles and vulnerable road users
Industrial inspection — Delineate defects, spills, cracks or irregular product areas.
Agriculture — Segment crops, weeds, fruit and plant diseases in field or drone videos.
Object Tracking and Multi-Object Tracking
Track one or more objects across consecutive frames while preserving a stable identifier for each instance. Object tracking annotations capture trajectories, entrances, exits, occlusions and reappearances for single-object and multi-object tracking models.
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 - Suivre véhicules, piétons et cyclistes dans des scènes de circulation complexes.
Retail — Analyse customer journeys and product interactions while maintaining identity across frames
Sports — Follow players, equipment and ball trajectories for automated statistics and tactical analysis.
Temporal Annotation and Event Classification
Mark the start and end of actions, events or operating states within a video timeline. Temporal annotation creates labelled segments for activity recognition, event detection, behaviour analysis and sequence classification.
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...)
Driver monitoring — Identify periods of attention, distraction, fatigue or unsafe behaviour.
Traffic analysis — Classify sequences as free-flowing, congested, blocked or incident-related.
Equipment monitoring — Label active, idle, maintenance and fault states over time.
Pose Estimation and Keypoint Tracking
Annotate anatomical or object keypoints across frames to model posture, articulation and movement over time. Pose estimation datasets may include full-body skeletons, hands, facial landmarks or custom keypoint structures.
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)
Sports — Analyse throwing,jumping, running and other technical movements
Workplace safety — Detect risky postures, falls and ergonomic issues.
Healthcare and rehabilitation — Measure posture, gait and joint range of motion.
Keyframe Annotation and Interpolation
Annotate selected keyframes and use interpolation to propagate boxes, polygons or keypoints across intermediate frames. Human annotators then review trajectories, shape changes, occlusions and identity switches to correct automation errors before delivery.
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
Long video sequences — Reducerepetitive manual labelling while preserving frame-level consistency
Vehicle and pedestrian tracking — Propagate trajectories between accurately labelled keyframes.
Pose estimation — Interpolate keypoints across continuous human movement and review ambiguous frames.
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 for video annotation?
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 video data annotation platforms of the market, adapted to your workflow and requirements
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.
Build Better Computer Vision Models with Expertly Annotated Video Data!
