Surveillance Camera Orientation Correction Using Pixel Pattern Feedback
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Solution Overview
Problem
Surveillance cameras often have configuration mismatches between their physical installation orientation and the configured orientation due to separate installation and enrollment processes, leading to undesirably rotated video streams.
Innovation Solution
Analyze pixel data from surveillance camera frames to determine the initial orientation setting, calculate an orientation delta, and generate a configuration change to correct the camera's orientation, which can be applied autonomously or with operator input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If separate installation and enrollment processes are used for surveillance cameras, then device complexity is reduced and ease of operation is improved, but configuration accuracy deteriorates leading to orientation mismatches
Solution Approach 1:
The system captures video frames from the camera, analyzes pixel data to detect orientation patterns (such as text orientation, object positions, or geometric features), and uses this feedback to automatically determine the camera's physical orientation. This closed-loop feedback mechanism corrects configuration errors by comparing detected orientation with configured orientation, resolving the mismatch caused by separate installation and enrollment processes
Solution Approach 2:
The camera system performs self-diagnosis and self-correction by automatically analyzing its own video output to detect orientation issues. The system autonomously identifies configuration mismatches without requiring manual inspection or intervention, enabling the camera to self-correct its orientation settings based on pattern recognition in captured frames
2Measurement precision
If manual inspection and correction of camera orientation is required, then measurement precision can be maintained, but productivity decreases and loss of time increases
Solution Approach 1:
The system replaces manual mechanical inspection processes with automated digital image analysis. Instead of requiring operators to physically inspect camera mounting and manually adjust settings, the system uses computer vision algorithms to analyze pixel data, automatically detect orientation patterns, and compute configuration corrections, thereby maintaining precision while dramatically improving productivity
Solution Approach 2:
The system introduces an intermediary automated analysis process between camera installation and configuration validation. Rather than directly requiring manual inspection, an automated intermediate step analyzes video frames to detect orientation, serving as a mediator that bridges the gap between physical installation and configuration verification, thereby eliminating the need for time-consuming manual processes
3Productivity
If automated orientation detection is implemented, then productivity is improved and loss of time is reduced, but device complexity increases
Solution Approach 1:
The system uses a multi-functional approach where the video processing pipeline serves multiple purposes: capturing surveillance footage, analyzing orientation patterns, and providing feedback for configuration correction. By making the video processing system universal and multi-functional, the patent avoids adding dedicated separate hardware or complex specialized systems, thereby improving productivity while minimizing the increase in device complexity
Data Source
AI summary
The present disclosure relates to systems, methods, and apparatuses for correcting a camera configuration used in fixed-position surveillance. A video stream generated by the surveillance camera is obtained. A pattern present in pixel data of a frame of the video stream is analyzed to derive an initial orientation setting of the surveillance camera. An orientation delta between the initial orientation setting of the surveillance camera and a desired orientation setting for the surveillance camera is determined based on the analyzing of the pattern present in the pixel data of the frame. A configuration change for the surveillance camera is generated. The configuration change is provided to the surveillance camera to cause the surveillance camera to update the initial orientation setting of the surveillance camera.