Camera Object Analysis Using Moment-Based Descriptors
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Solution Overview
Problem
Camera surveillance systems face challenges in accurately recognizing, detecting, and categorizing objects due to factors like illumination, scale, and camera rotation, limiting their intelligence and effectiveness in automated video analysis.
Innovation Solution
A computerized method and system that calculates radial and central moments of image features, generating normalized descriptors to recognize, detect, and categorize objects, while being invariant to scale and rotation, and robust to illumination changes, using integral image representation for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object analysis methods are used in camera systems, then the system structure remains simple, but the object recognition, detection, and categorization accuracy deteriorates under varying illumination, scale, and rotation conditions
Solution Approach 1:
The patent applies parameter changes by transforming image data into moment-based features (central moments, radial moments) that are invariant to scale and rotation. By changing the representation parameters from raw pixel values to moment descriptors, the system achieves robustness against illumination, scale, and rotation variations without requiring complex multi-camera setups or post-processing corrections
Solution Approach 2:
The patent substitutes mechanical/physical correction methods (such as physically adjusting camera orientation or using multiple cameras to compensate for rotation) with a computational approach using moment invariants. Instead of mechanically addressing the rotation and scale issues, the system uses mathematical transformations of image moments to achieve invariance, replacing physical system complexity with algorithmic elegance
2Productivity
If automated video analysis is implemented, then productivity increases, but the ability to accurately recognize and detect objects deteriorates due to illumination, scale, and rotation factors
Solution Approach 1:
The patent changes the feature representation parameters to moment-based descriptors that inherently provide invariance properties. Central moments provide scale invariance, while radial moments and normalized descriptors provide rotation invariance. This parameter transformation enables automated analysis to maintain high reliability across varying conditions without requiring manual intervention or complex adaptive algorithms
Solution Approach 2:
The patent creates a transformed copy of the original image data in the form of moment descriptors. Instead of directly analyzing raw pixel data which is sensitive to transformations, the system creates a moment-based representation that captures essential object characteristics while being invariant to illumination, scale, and rotation changes, thereby maintaining detection reliability in automated processing
3Reliability
If moment-based descriptors are calculated for each pixel, then object analysis robustness improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image into regions of interest and calculating moments for each region rather than processing every pixel individually with full complexity. The method segments the computational task into manageable regions while maintaining the robustness benefits of moment-based descriptors, thereby reducing overall computational complexity
Solution Approach 2:
The patent changes the order of operations and parameters by pre-calculating image moments from the image data, then using these moments to generate descriptors. This parameter transformation approach is more computationally efficient than traditional pixel-by-pixel comparison methods, as moment calculations can be performed once and reused for multiple analysis operations
Data Source
AI summary
A system, method and program product for camera-based object analyses including object recognition, object detection, and/or object categorization. An exemplary embodiment of the computerized method for analyzing objects in images obtained from a camera system includes receiving image(s) having pixels from the camera system; calculating a pool of features for each pixel; then deriving either a pool of radial moment of features from the pool of features and a geometric center of the image(s) or a pool of central moments of features from the pool of features; then calculating a normalized descriptor, based on an area of the image(s) and either of the derived pool of moments of features; and then based on the normalized descriptor, a computer then either recognizes, detects, and/or categorizes an object(s) in the image(s).