Video Annotation & Segmentation

This task carves videos into pieces—think “cat” boxed in a home clip or “run” split from a chase scene (e.g., “sky” as backdrop, “door” as object)—to teach AI the parts of the picture. Our team labels these slices, boosting video smarts for all kinds of uses.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are crucial in orchestrating the annotation and structuring of data for Video Annotation & Segmentation within video processing workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label and segment video datasets that enhance AI’s understanding of visual content.

Training and Onboarding

PMs design and implement training programs to ensure workers master object labeling, action segmentation, and background tagging. For example, they might train teams to box “car” in a street shot or split “jump” in a sports clip, guided by sample footage and annotation standards. Onboarding includes hands-on tasks like segmenting frames, feedback loops, and calibration sessions to align outputs with AI video goals. PMs also establish workflows, such as multi-pass reviews for detailed scenes.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 15,000 video frames) and set metrics like object accuracy, segment precision, or background consistency. They track progress via dashboards, address labeling errors, and refine methods based on worker insights or evolving video needs.

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high precision for small objects) into actionable annotation tasks. They also manage timelines, ensuring labeled datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The annotators, taggers, or video analysts perform the detailed work of labeling and segmenting video datasets for AI training. Their efforts are visual and precise, requiring attention to detail and context.

Labeling and Tagging

For video data, we might tag objects as “tree” or “person.” In complex tasks, they label segments like “walking” or “shadow shift.”

Contextual Analysis

Our team breaks down footage, boxing “bike” in a race or segmenting “wave” in a crowd, ensuring AI sees every layer clearly.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “dog” as “cat”) or sloppy segments (e.g., blurry edges), maintaining dataset quality and usability.

Edge Case Resolution

We tackle complex cases—like overlapping objects or rapid cuts—often requiring frame-by-frame tweaks or escalation to video experts.

We can quickly adapt to and operate within our clients’ video platforms, such as proprietary annotation tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of frames per shift, depending on the complexity of the footage and annotations.

Data Volumes Needed to Improve AI

The volume of annotated video data required to enhance AI systems varies based on the diversity of content and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional video model might require 5,000–20,000 annotated frames per category (e.g., 20,000 daily life clips). For varied or intricate scenes, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 frames per issue (e.g., mis-segmented actions) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., city-wide monitoring) require datasets in the hundreds of thousands to handle edge cases, rare objects, or new contexts. An annotation effort might start with 100,000 frames, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky frames for further labeling. This reduces total volume but requires ongoing effort—perhaps 500–2,000 frames weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and visual precision across datasets.

Multilingual & Multicultural Video Annotation & Segmentation

We can assist you with video annotation and segmentation across diverse linguistic and cultural landscapes.

Our team is equipped to label and segment video data from global contexts, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: