Pose Estimation & Gesture Recognition

This task maps bodies in motion—think “arm raised” tracked in a gym clip or “thumbs up” tagged in a VR feed (e.g., “knees bent” marked, “wave” pinned)—to teach AI how we move and signal. Our team labels these poses, powering systems that read us loud and clear.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the annotation and structuring of data for Pose Estimation & Gesture Recognition within video processing workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label video datasets that enhance AI’s ability to interpret human poses and gestures accurately.

Training and Onboarding

PMs design and implement training programs to ensure workers master skeletal mapping, gesture tagging, and posture analysis. For example, they might train teams to mark “elbow bend” in a workout or tag “point” in a demo, guided by sample footage and movement standards. Onboarding includes hands-on tasks like annotating frames, feedback loops, and calibration sessions to align outputs with AI interaction goals. PMs also establish workflows, such as multi-angle reviews for complex poses.

Task Management and Quality Control

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

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high fidelity for subtle gestures) 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 structuring video datasets for AI training. Their efforts are visual and anatomical, requiring precision and movement awareness.

Labeling and Tagging

For video data, we might tag poses as “standing” or “hand clap.” In complex tasks, they label joints like “shoulder angle” or “finger curl.”

Contextual Analysis

Our team tracks bodies, mapping “squat” in a fitness clip or tagging “swipe” in a VR scene, ensuring AI reads every twist and turn.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “sit” as “stand”) or unclear poses (e.g., occluded limbs), maintaining dataset quality and clarity.

Edge Case Resolution

We tackle complex cases—like overlapping bodies or rapid gestures—often requiring frame-by-frame tweaks or escalation to movement experts.

We can quickly adapt to and operate within our clients’ video platforms, such as proprietary pose 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 poses 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 movements and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional pose model might require 5,000–20,000 annotated frames per category (e.g., 20,000 workout clips). For varied or subtle gestures, 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., misread joints) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., VR ecosystems) require datasets in the hundreds of thousands to handle edge cases, rare poses, or new gestures. 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 poses 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 motion precision across datasets.

Multilingual & Multicultural Pose Estimation & Gesture Recognition

We can assist you with pose estimation and gesture recognition across diverse linguistic and cultural landscapes.

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

We work in the following languages: