Structured Labelling for Motion and Object Interpretation

Video annotation involves adding labels to video footage so machine learning systems can interpret scenes, objects, and movements. This process helps build training datasets for computer vision applications used in transportation technology, healthcare imaging, aerial mapping, manufacturing automation, and retail behaviour analysis.

Accurate frame-by-frame annotation enables models to understand motion, object interaction, spatial relationships, and temporal activity patterns.

Annotation Methods

Bounding Boxes

Rectangular outlines are applied around objects in each frame to help systems recognize and track them. People often use this method to vehicles, pedestrians, and other moving objects in motion-heavy environments.

Polygon Annotation

Points are placed around the perimeter of an object to trace its exact shape. This is useful when working with irregular or non-linear objects.

Semantic Segmentation

The system assigns each pixel in a video frame to a category. This produces a highly detailed understanding of surfaces, structures, and object boundaries.

Keypoint Annotation

Researchers or analysts mark specific points on essential features, such as joints, mechanical hinges, or facial details. They use these points to analyze movement or structural alignment.

Landmark Annotation

Systems use consistent markers to indicate key facial or object reference points for recognition, comparison, or alignment tasks.

3D Cuboid Annotation

Three-dimensional box shapes are drawn around objects to capture length, width, and height. This supports distance measurement and spatial depth interpretation.

Polyline Annotation

People draw lines to mark boundaries, road edges, pathways, or structural layouts. This practice appears often in navigation systems and mapping workflows.

Rapid Annotation with Interpolation

Where motion is predictable, interpolation tools accelerate labelling by extending annotation across multiple frames with adjustments made only when the object shifts.

Process Workflow

1. Initial Project Review – Clarifies expectations, formats, and objectives.
2. Training Phase – The team reviews the task instructions and examples to maintain consistency.
3. Workflow Setup – We arrange the tools, steps, and checkpoints to create a clear progression.
4. Feedback and Adjustment – Ongoing refinements ensure alignment with project needs.
5. Final Review – The team reviews the deliverables for accuracy and consistency across the dataset.

Common Application Areas

• Autonomous Vehicles and Transportation Systems

Labelling vehicles, lanes, signals, and hazards for navigation models.

• Medical Analysis and Healthcare Footage

Identifying areas of interest in clinical or diagnostic video.

• Geospatial and Aerial Footage

Interpreting satellite, drone, or aircraft video for land, resource, or development studies.

• Security and Public Surveillance

Analyzing recorded footage for identity and activity recognition tasks.

• Manufacturing and Robotics

Assisting automated systems in identifying patterns in assembly or inspection footage.

• Retail and Commerce Environments

Observing interactions with shelves, products, and store layouts.

Typical Use Case Scenarios

• Marking objects in surveillance video for tracking patterns over time
• Teaching vehicle systems to detect roadway conditions from rear or side cameras
• Labelling lane boundaries for navigation support
• Identifying facial reference points for recognition models
• Helping production robots detect irregular items or actions
• Tracking crop conditions in agricultural drone footage
• Reviewing movement form in athletic and training environments

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