Video Data Services
Video Data Services provide structured video datasets for AI applications in object detection, motion analysis, and real-time surveillance. These services are critical for training AI models in autonomous vehicles, security monitoring, and media content tagging.
Where Open Active Comes In - Experienced Project Management
Project managers (PMs) are essential in orchestrating the development and enhancement of Video Data AI systems.
We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the video data that powers these systems.
Training and Onboarding
PMs design and implement training programs to ensure workers understand video annotation standards, temporal dynamics, and project goals. For example, in autonomous vehicle annotation, PMs might train workers to track moving objects frame-by-frame, using sample clips and guidelines. Onboarding includes hands-on tasks like segmenting or tagging, feedback sessions, and calibration exercises to align worker outputs with AI needs. PMs also establish workflows, such as multi-tier reviews for complex medical videos.
Task Management and Quality Control
Beyond onboarding, PMs define task scopes (e.g., annotating 1,000 hours of video) and set metrics like frame-level accuracy, tracking consistency, or event detection rates. They monitor progress via dashboards, address bottlenecks, and refine guidelines based on worker insights or client priorities.
Collaboration with AI Teams
PMs connect video data curators with machine learning engineers, translating technical requirements (e.g., millisecond precision in tracking) into actionable tasks. They also manage timelines to sync data delivery with AI training cycles.
We Manage the Tasks Performed by Workers
The annotators, trackers, or segmenters perform the detailed work of preparing high-quality video datasets. Their efforts are meticulous and motion-focused, requiring precision and temporal awareness.
Common tasks include:
Labeling and Tagging
For object tracking, we might tag a “bicycle” moving across frames. In event detection, they label moments like “goal scored” with timestamps.
Contextual Analysis
For video summarization, our team identifies key scenes, tagging “car crash” or “celebration.” In pose estimation, they analyze joint movements to tag “jumping.”
Flagging Violations
In medical annotation, our employees and subcontractors flag unclear frames (e.g., obscured tools), ensuring reliable data. In facial landmark annotation, they mark inconsistent tracking.
Edge Case Resolution
We tackle complex cases—like fast-moving objects or overlapping gestures—often requiring discussion or escalation to video specialists.
We can quickly adapt to and operate within our clients’ annotation platforms, such as proprietary video tools or industry-standard systems, efficiently processing batches of video ranging from dozens to thousands of frames per shift, depending on task complexity.
Data Volumes Needed to Improve AI
The volume of curated video data required to train and refine Video Data AI systems is immense, driven by the complexity of motion and context. While specifics vary by task and model, general benchmarks include:
Baseline Training
A functional model might require 5,000–20,000 annotated video frames per category (e.g., 20,000 frames of pedestrian movement). For tasks like medical video, this could rise to 50,000 frames.
Iterative Refinement
To boost accuracy (e.g., from 80% to 95%), an additional 3,000–15,000 frames per issue (e.g., misrecognized actions) are often needed. For example, refining gesture recognition might demand 10,000 new frames.
Scale for Robustness
Large-scale systems (e.g., autonomous driving) require datasets in the hundreds of thousands or millions of frames to cover edge cases, lighting, or angles. An object tracking model might start with 100,000 frames, expanding by 30,000 annually.
Active Learning
Advanced systems use active learning, where AI flags uncertain frames for review. This reduces volume but requires ongoing curation—perhaps 500–3,000 frames weekly—to maintain performance.
The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and quality.
Multilingual & Multicultural Video Data Services
We can assist you with your video data service needs across diverse linguistic and cultural contexts.
Our team is equipped to annotate and process video data for global applications, ensuring culturally relevant and accurate datasets tailored to your objectives.
We work in the following languages: