Site-Calibrated Object Detection Rules for Fewer Surveillance False Alarms

The patent segments the detection process into two distinct stages: (1) real-time object detection using a practical model with limited resources, and (2) post-processing rule-based validation using scene description parameters. This segmentation allows the system to maintain fast real-time detection while improving accuracy through additional rule-based filtering in the post-processing stage, resolving the contradiction between speed and accuracy.

The patent performs preliminary calibration during a calibration period to determine scene description parameters (object data, region identifiers, time periods) before actual detection begins. This preliminary action creates a customized rule set for the specific camera location that is then applied during real-time detection, enabling the system to achieve high accuracy without compromising processing speed during operational phases.

The patent introduces rule-based post-processing as an intermediary mechanism between the edge device's object detector and the final detection results. This intermediary layer uses lightweight rule evaluations based on pre-determined scene description parameters to improve detection reliability without requiring additional heavy compute resources at the edge device, thus maintaining real-time capability while enhancing reliability.

The patent creates a simplified copy of scene understanding capabilities through rule-based post-processing rather than attempting to run full-scale AI models at the edge. By copying essential scene context information into pre-determined rules during calibration, the system achieves reliable detection at the edge device with limited resources, avoiding the need for cloud-based processing while maintaining accuracy.

The patent implements feedback through rule-based post-processing that evaluates detected objects against scene-specific rules determined during calibration. This feedback mechanism filters out false positives by checking whether detected objects conform to expected scene patterns (e.g., objects in appropriate regions, during appropriate time periods), thereby reducing false alarm rates while maintaining high detection accuracy.

The patent changes the parameters used for detection evaluation by incorporating scene description parameters (region identifiers, time periods, object data) into the detection process. Instead of relying solely on raw object detection model output, the system evaluates detections against multiple scene-specific parameters, significantly reducing false alarms while maintaining high accuracy for true detections.

The patent performs preliminary calibration during an initial period to determine all scene description parameters and create customized rule sets for each camera location before actual detection begins. This preliminary action consolidates the complexity into a one-time setup process, allowing the rule sets to be stored and reused during operational phases without adding ongoing complexity to the detection process.

The patent creates a universal calibration framework that can be applied to any camera location. The same calibration process and rule structure are universally applicable across different cameras and locations, requiring only that the system go through the calibration process to adapt to specific local conditions. This universality reduces overall system complexity by using a standardized approach rather than requiring custom solutions for each location.