Security & Surveillance AI
Security & Surveillance AI focuses on enhancing public safety through advanced AI technologies that monitor, detect, and respond to threats in real-time. By leveraging data from various sources such as cameras, sensors, and audio systems, AI models can identify suspicious behaviors, improve situational awareness, and support predictive security measures. This service is crucial for applications in law enforcement, public safety, and private security, helping mitigate risks and prevent incidents before they occur.
Where Open Active Comes In - Experienced Project Management
Project managers (PMs) are pivotal in orchestrating the development and enhancement of Security & Surveillance AI systems.
We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the data that powers these critical systems.
Training and Onboarding
PMs design and implement training programs to ensure workers understand security protocols, data sensitivity, and annotation goals. For example, in suspicious behavior detection, PMs might train workers to recognize subtle cues, using sample videos and behavioral guidelines. Onboarding includes hands-on tasks like tagging audio or video, feedback sessions, and calibration exercises to align worker outputs with AI needs. PMs also establish workflows, such as escalated reviews for high-stakes annotations like gunshots.
Task Management and Quality Control
Beyond onboarding, PMs define task scopes (e.g., labeling 10,000 traffic frames) and set metrics like precision, recall, or false positive rates. They monitor progress via dashboards, address inefficiencies, and refine guidelines based on worker feedback or evolving security requirements.
Collaboration with AI Teams
PMs bridge the gap between data curators and machine learning engineers, translating technical needs (e.g., real-time detection thresholds) into actionable tasks. They also manage timelines to ensure data delivery aligns with AI deployment cycles.
We Manage the Tasks Performed by Workers
The annotators, taggers, or analysts perform the detailed work of preparing high-quality datasets for security and surveillance. Their efforts are precise and context-driven, requiring vigilance and accuracy.
Common tasks include:
Labeling and Tagging
For gunshot detection, we might tag an audio clip as “firearm discharge” with a timestamp. In license plate recognition, they label plates with text like “ABC-123.”
Contextual Analysis
For crowd monitoring, our team assesses density, tagging areas as “congested” or “clear.” In suspicious behavior detection, they analyze actions to tag “running” or “loitering.”
Flagging Violations
In facial recognition, our employees and subcontractors flag blurry or obscured faces, ensuring usable data. In traffic monitoring, they mark anomalies like “stalled vehicle.”
Edge Case Resolution
We handle complex cases—like muffled gunshots or partially visible plates—often requiring discussion or escalation to security experts.
We can quickly adapt to and operate within our clients’ annotation platforms, such as proprietary security tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of items per shift, depending on task complexity.
Data Volumes Needed to Improve AI
The volume of curated data required to train and refine Security & Surveillance AI systems is substantial, driven by the need for precision and real-world variability.
While specifics vary by task and model, general benchmarks include:
Baseline Training
A functional model might require 5,000–20,000 labeled samples per category (e.g., 20,000 annotated gunshots). For tasks like facial recognition, this could rise to 50,000 to cover diverse conditions.
Iterative Refinement
To improve accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 samples per issue (e.g., missed suspicious behaviors) are often needed. For example, refining traffic monitoring might demand 5,000 new frames.
Scale for Robustness
Large-scale systems (e.g., city-wide surveillance) require datasets in the hundreds of thousands to cover edge cases, lighting, or rare events. A behavior detection model might start with 100,000 frames, expanding by 25,000 annually.
Active Learning
Advanced systems use active learning, where AI flags uncertain data for review. This reduces volume but requires ongoing curation—perhaps 500–2,000 samples weekly—to maintain performance.
The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and reliability.
Multilingual & Multicultural Security & Surveillance AI
We can assist you with your security and surveillance AI needs across diverse linguistic and cultural contexts.
Our team is equipped to curate and process data for global security applications, ensuring accurate and culturally relevant datasets tailored to your objectives.
We work in the following languages: