Can AI Video Monitoring eliminate false alarms?

AI Video Monitoring can significantly reduce false alarms but cannot eliminate them.

How AI Video Monitoring Reduces False Alarms:

AI-Based Filtering: AI Video Monitoring uses AI video analytics to filter out “False Positive” alarms by distinguishing between genuine threats and benign triggers like moving trees, animals, or changing light conditions.

Advanced Object Classification: AI video analytics can classify detected objects (humans, vehicles, animals, etc.) to determine if an object is a threat.

Cloud-Based Continuous Learning: AI algorithms continuously update and refine their detection accuracy, helping to reduce false triggers over time.

Scene Noise Cancelling: AI algorithms filter out environmental noise, such as reflections, weather changes, and small moving objects, reducing unnecessary alarms.

Limitations

No AI is Infallible: AI systems can still misclassify threats or fail to detect actual incidents, similar to how AI, like ChatGPT, sometimes provides incorrect information.

Environmental Challenges: Poor lighting, camera placement, and occlusions can affect detection accuracy.

Potential Missed True Positives: AI can sometimes fail to detect security threats due to biases in training data or real-world complexities.

Conclusion

While AI video monitoring cannot eliminate false alarms, it can drastically reduce them, enhance security operational efficiency, improve response times, and minimise operator fatigue. Human oversight and complementary security measures remain essential for optimal performance.