Active Learning in Surveillance AI

Active Learning with Operator Feedback Loops

Active learning with operator feedback loops is a powerful technique used in surveillance AI.

Improving Models Through Human Input

Operator feedback – confirming, dismissing, or annotating – continuously improves the accuracy of these models.

Active learning strategically selects uncertain events for human review, concentrating labeling efforts on areas where they are most impactful.

Pipelines for Continuous Improvement

These systems utilize pipelines to capture corrections, periodically retrain the models, and meticulously track performance gains.

Robust governance measures prevent model drift and mitigate the risk of biased behaviors emerging within the AI system.

Frequently asked questions