Event Detection & Video Summarization

This task pinpoints the juice in videos—think “goal scored” tagged in a match or “door opened” flagged in a feed (e.g., “handshake” marked, “scene fades” noted)—to boil hours into highlights. Our team labels these beats, powering AI to spot and sum up what matters.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are key in orchestrating the annotation and structuring of data for Event Detection & Video Summarization 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 detect events and summarize content effectively.

Training and Onboarding

PMs design and implement training programs to ensure workers master event tagging, interaction labeling, and transition spotting. For example, they might train teams to mark “tackle” in a game or “alarm triggered” in surveillance, guided by sample footage and event protocols. Onboarding includes hands-on tasks like annotating key moments, feedback loops, and calibration sessions to align outputs with AI insight goals. PMs also establish workflows, such as multi-pass reviews for fast-paced clips.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 15,000 video segments) and set metrics like event accuracy, summary relevance, or transition precision. 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 recall for rare events) 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 summarizing video datasets for AI training. Their efforts are visual and temporal, requiring attention to action and context.

Labeling and Tagging

For video data, we might tag moments as “collision” or “crowd cheer.” In complex tasks, they label transitions like “cut to next” or “action peak.”

Contextual Analysis

Our team flags events, tagging “pass made” in a play or “light flickers” in a room, ensuring AI catches the story in every frame.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “jump” as “fall”) or missed events (e.g., subtle shifts), maintaining dataset quality and insight.

Edge Case Resolution

We tackle complex cases—like overlapping actions or blurry footage—often requiring frame-by-frame scrutiny 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 segments per shift, depending on the complexity of the footage and events.

Data Volumes Needed to Improve AI

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

Baseline Training

A functional event model might require 5,000–20,000 annotated segments per category (e.g., 20,000 security clips). For varied or rare events, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 segments per issue