Sunlight Overexposure Detection in Surveillance Imaging
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Solution Overview
Problem
In environments with varying lighting conditions, such as materials handling facilities, it is challenging to accurately determine overexposed regions in images due to excessive light from different light sources, leading to false detections and loss of data, especially with bright objects or reflective surfaces.
Innovation Solution
The method involves generating a baseline model based on image data under normal conditions to mask out saturated pixels and using a radiance detection service that processes camera parameters and image data to identify overexposed areas, applying Gaussian blur to smooth pixel values, and determining camera radiance to distinguish between overexposure caused by sunlight or bright objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital cameras are used to capture imaging data in environments with varying lighting conditions, then surveillance and monitoring operations can be conducted, but regions of images become overexposed by excessive light from different light sources leading to loss of information
Solution Approach 1:
The system performs preliminary actions by capturing multiple images at different exposure settings before the overexposure problem fully manifests. It also pre-identifies bright light sources and creates masks in advance to protect vulnerable image regions during processing
Solution Approach 2:
The system employs feedback mechanisms by analyzing captured images to identify overexposed regions, then using this information to adjust processing parameters and generate corrective masks. The feedback loop continues by comparing processed results with original images to refine the detection and correction algorithms
2Measurement precision
If image processing is performed to identify overexposed regions, then accurate detection can be achieved, but false detections occur due to bright objects or reflective surfaces
Solution Approach 1:
The system segments the image processing task into multiple independent components: bright light source detection, bright object identification, reflective surface recognition, and overexposed region masking. Each segment handles a specific aspect of the problem, reducing false detections by focusing on distinct characteristics
Solution Approach 2:
The system applies local quality principles by creating spatially varying masks that are applied differently to different regions of the image. Bright light sources receive one type of processing, while bright objects and reflective surfaces receive different processing strategies based on their local characteristics
3Area of stationary object
If multiple light sources are present in different areas of materials handling facilities, then comprehensive monitoring coverage is achieved, but determining events in overexposed regions becomes difficult
Solution Approach 1:
The system transitions from two-dimensional image space to three-dimensional solution space by incorporating spatial coordinates, mask layers, and processing parameters. This dimensional expansion allows the system to preserve and analyze information from overexposed regions through multiple computational layers rather than losing it in the original image plane
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively reduces false positives by differentiating between overexposure due to sunlight and bright objects, ensuring accurate image data representation and maintaining data integrity in varying lighting conditions.
Implementation Method 1
an imaging device may be aligned and configured to capture imaging data such as still or moving images of actions or events occurring within their respective fields of view
Implementation Method 2
camera parameters (e.g., integration time and gain of the camera) are used with the image data from the camera to determine a radiance value for each pixel of the image data
Implementation Method 3
applying Gaussian blur to smooth pixel values
Data Source
AI summary
Described is a method for processing image data to determine if a portion of the image data is affected due to sunlight. In some implementations, image data is sent to an image data store and camera parameters are sent to a radiance detection service. The radiance detection service, upon receiving the camera parameters, retrieves the image data, converts the image data to gray-scale and processes the image data based on the camera parameters to determine a radiance value for the camera. The radiance value may be compared to a baseline radiance value to determine if sunlight is represented in the image data. In some implementations, a baseline model may be developed for the camera and used to cancel out any pixels of the image data that are overexposed under normal or baseline conditions. Likewise, a foreground model may be generated to detect any objects in the image data for which corresponding pixel values should not be considered for determining if sunlight is represented in the image data.