How does AI differentiate between human movement and environmental factors like rain, wind, or animals?

Using video analytics, including object detection, classification, and behaviour analysis, AI differentiates between human movement and environmental factors like rain, wind, or animals. Here’s how it works:

Object Detection & Classification: AI-powered surveillance systems use deep learning models to identify and classify objects in video. These models, trained on extensive datasets, can distinguish between humans, vehicles, animals, and non-relevant environmental factors like rain, shadows, or moving leaves.

Behavioural Analysis & Motion Filtering: AI analyses movement patterns to determine whether an object’s motion is characteristic of a human (e.g., walking upright, consistent stride patterns) or something else (e.g., erratic movement due to wind, birds, or reflections). For instance, Evolon’s AI algorithms can differentiate between people and vehicles while filtering out motion from rain, shadows, and small animals.

Scene Noise-Canceling & Environmental Adaptation: AI systems apply scene noise-cancelling techniques to reduce false alarms caused by environmental conditions such as fog, wind-blown objects, and shifting lighting conditions. These algorithms adapt to changing environments, ensuring reliable detection even in outdoor settings.

Machine Learning for Continuous Improvement: AI-enhanced surveillance solutions continuously learn from real-world data. This allows the system to improve accuracy over time by refining its ability to differentiate between true security threats and false positives.

Combining these capabilities, AI-based video analytics can significantly reduce false alarms while ensuring that legitimate security threats, such as unauthorised human movement, are accurately detected and escalated.