Object Tracking & Action Recognition

This task trails motion like a hawk—think “runner” tracked across a field or “wave” tagged in a crowd (e.g., “car” followed frame-by-frame, “jump” flagged mid-air)—to teach AI the dance of life. Our team labels these moves, powering systems that see and react in real time.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are pivotal in orchestrating the annotation and structuring of data for Object Tracking & Action 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 track objects and recognize actions accurately.

Training and Onboarding

PMs design and implement training programs to ensure workers master object tracking, action tagging, and motion continuity. For example, they might train teams to follow “bike” through traffic or tag “kick” in a game, guided by sample footage and motion protocols. Onboarding includes hands-on tasks like annotating sequences, feedback loops, and calibration sessions to align outputs with AI perception goals. PMs also establish workflows, such as multi-frame reviews for smooth tracking.

Task Management and Quality Control

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

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high fidelity for fast moves) 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 dynamic, requiring precision and motion awareness.

Labeling and Tagging

For video data, we might tag objects as “person” or “ball.” In complex tasks, they label actions like “turn” or “running stride.”

Contextual Analysis

Our team tracks motion, following “dog” across a park or tagging “push” in a scuffle, ensuring AI reads every step and gesture.

Flagging Violations

Workers review datasets, flagging mis-tracks (e.g., “cat” lost mid-frame) or wrong actions (e.g., “walk” as “run”), maintaining dataset quality and flow.

Edge Case Resolution

We tackle complex cases—like occlusions or rapid shifts—often requiring frame-by-frame adjustments or escalation to motion experts.

We can quickly adapt to and operate within our clients’ video platforms, such as proprietary tracking 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 motion 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 tracking model might require 5,000–20,000 annotated frames per category (e.g., 20,000 sports clips). For varied or fast-paced actions, 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., lost tracks) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., city surveillance) require datasets in the hundreds of thousands to handle edge cases, rare motions, or new scenarios. 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 motions 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 Object Tracking & Action Recognition

We can assist you with object tracking and action 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: