Alarm Classification Using CNN Visual Analysis

The patent replaces primitive motion detection software with a convolutional neural network (CNN) based computer vision system. This substitution transforms the detection mechanism from simple threshold-based motion sensing to intelligent image processing that can distinguish between actual threats and environmental disturbances, thereby maintaining ease of manufacture while dramatically improving reliability and reducing false alarms.

The system changes the detection parameters from simple motion thresholds to complex visual feature analysis using CNN. By transforming the detection approach from measuring basic motion parameters to analyzing visual patterns, object characteristics, and contextual information, the system achieves higher reliability without complicating the physical manufacturing process.

The patent implements a universal CNN-based processing platform that can handle multiple detection tasks (person detection, vehicle detection, animal detection, object recognition) through a single system architecture. This multi-functional approach achieves high reliability across various security scenarios without proportionally increasing device complexity, as the same core processing engine serves multiple detection purposes.

The system introduces an intermediary processing layer (the CNN algorithm) between the camera input and the alarm output. This intermediary performs intelligent analysis of visual data, filtering out false alarms before they reach the alarm generation stage. The intermediary layer manages the complexity by providing a structured processing pipeline that can be implemented using standard computing hardware.

The system performs preliminary classification of detected objects using the CNN to determine whether an alarm is warranted before completing full processing. By pre-assessing the significance of detected objects and filtering out obvious false alarms early in the processing pipeline, the system maintains high measurement precision for critical detections while improving overall productivity by avoiding unnecessary processing of non-threatening events.

The patent applies partial processing to different alarm scenarios based on their importance. For high-confidence detections, full visual data processing is performed to ensure measurement precision. For low-confidence or clearly benign detections, simplified processing paths are used. This selective approach balances measurement precision with processing throughput, maintaining productivity while achieving accurate classification where it matters most.