Dynamic Surveillance Camera Calibration for False Detection Reduction
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Solution Overview
Problem
Residential-based commercial intelligent surveillance systems face high false alarm rates due to false detections and misclassification of objects in camera video feeds, leading to unnecessary alerts and costs.
Innovation Solution
Dynamic calibration of surveillance devices using deep learning to automatically correct camera miscalibration and account for environmental factors, by receiving camera settings and uploaded images, determining false positives, and adjusting camera parameters to reduce false detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera parameters are manually calibrated, then initial detection accuracy is achieved, but false positive detections increase due to environmental changes and miscalibration over time
Solution Approach 1:
The patent implements dynamic camera calibration that automatically adjusts camera parameters based on real-time environmental conditions and detected objects. The system transitions from static manual calibration to continuous adaptive calibration, where camera settings are dynamically modified to account for changes in lighting, weather, and scene composition, thereby maintaining detection accuracy while reducing false positives caused by environmental variations
Solution Approach 2:
The system employs feedback mechanisms where detection results are continuously monitored and used to trigger recalibration when false positive rates exceed thresholds. The server receives detection images, identifies false positives, and automatically initiates camera parameter adjustments. This closed-loop feedback ensures that calibration errors are corrected in real-time, preventing the accumulation of detection errors that would occur with static calibration
2Reliability
If deep learning-based dynamic calibration is implemented, then false positive detections are reduced, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces a server as an intermediary between the camera and the calibration process. The server handles the computationally intensive deep learning-based analysis of detection images and camera parameter optimization, while the camera itself performs only basic object detection. This intermediary architecture distributes system complexity, allowing the camera to remain relatively simple while the server performs the sophisticated calibration computations
Solution Approach 2:
The system uses detection images as copies of the actual scene to perform calibration analysis without requiring physical test objects or complex calibration targets. By analyzing copies (images) of the surveillance scene, the system can determine camera parameter adjustments needed to reduce false positives, avoiding the need for complex physical calibration apparatus while maintaining calibration accuracy
3Measurement precision
If camera calibration is frequently adjusted, then detection accuracy is maintained, but data transmission and processing loads increase
Solution Approach 1:
The system implements partial calibration adjustments by only modifying camera parameters when false positive rates exceed specific thresholds, rather than continuously adjusting all parameters. The server selectively triggers recalibration based on detected error patterns, performing calibration actions only when necessary to maintain detection accuracy. This partial action approach maintains precision while minimizing unnecessary data transmission and processing loads associated with frequent full-scale recalibration
Data Source
AI summary
Methods and systems including computer programs encoded on a computer storage medium, for receiving, from a camera, a set of images in which the camera detected a particular event based on a first set of camera settings, determining that false detections in the set of images made by the camera based on the first set of camera settings were caused by localized errors, and in response, generating a second set of camera settings based on the localized errors and providing the second set of camera settings to the camera.