Hansem Global prepares and labels image and video data so robots and autonomous systems can learn to recognize objects, actions, and changes in real-world environments.Image and video data preparation and labeling for robots and autonomous systems to recognize real-world objects, actions, and changes.
Physical AI projects require different labeling criteria depending on the environment, objects, actions, and events the system needs to recognize. We work with project-defined guidelines to classify and label image and video data according to the required categories, attributes, and levels of detail.
Physical AI data goes beyond identifying what appears in an image. Depending on the project, datasets may also need to capture what people or equipment are doing, how objects interact, and when important states or events occur. We organize these elements into structured labels that can be used for training and evaluation.
Large datasets require more than annotation capacity. We organize trained teams, clear work instructions, PM-led workflows, sample checks, and review processes so that the same labeling criteria can be applied consistently across large volumes of image and video data.
Labeling results are reviewed for missing annotations, incorrect classifications, and inconsistencies between workers or batches. Multi-stage quality checks help maintain accuracy and consistency, while ISO/IEC 27001-based information security controls support the secure handling of client data throughout the project.
We identify and label key objects in image and video data collected from real-world environments, including products, equipment, parts, obstacles, and people. Depending on the project, we can classify object types, identify locations, and apply additional attributes defined in the annotation guidelines.
We also organize relevant environmental information, such as work areas, surrounding conditions, or recurring object categories, so that the dataset remains consistent across large volumes of visual data.
Structured labels that help AI recognize objects and environments consistently.
We identify and label movements, actions, and task sequences performed by people or equipment in video data. Project-defined actions such as moving, approaching, handling, operating, or completing a task can be annotated according to their start and end points or task stages.
This helps organize time-based behavioral data so that AI systems can learn not only what is present in a scene, but also what is happening and how activities progress over time.
Action data organized around movement, task flow, and real-world behavior.
We label changes in object states, working conditions, and key events that occur over time. These can include task start and completion, normal and abnormal conditions, specific event triggers, changes in object position, or other project-defined states.
By applying consistent criteria to these changes and events, we help structure image and video data so that AI systems can distinguish meaningful conditions and transitions in real-world environments.
State changes and key events structured as usable training data.
We review labeled datasets against project guidelines to identify missing annotations, incorrect classifications, and inconsistencies between workers or batches. Quality control can include worker training, sample review, staged checks, issue tracking, and correction of identified errors.
For large-scale projects, these processes help maintain the same annotation standards throughout the dataset and reduce variation as the number of workers, files, or project phases increases.
Consistent labeling quality maintained through clear standards and systematic review.
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