Object Detection Using Symmetry Filters for Illumination Robustness
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Solution Overview
Problem
Existing object detection algorithms face challenges in robustness to illumination changes, applicability across object classes, view and pose independence, scale independence, and speed and efficiency, particularly due to variations in ambient conditions and intra-class variations within object categories.
Innovation Solution
The method employs a symmetry detection filter to identify locally maximal symmetry scores in image-difference signatures, utilizing motion and structural symmetries to detect object location and scale, which are invariant to illumination changes and object pose, and can be efficiently computed using a square detection window and multi-resolution image pyramids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learning-based methods are used for object detection, then detection accuracy and robustness improve, but computational resource consumption and processing time increase
Solution Approach 1:
The patent replaces complex learning-based computational systems with a simpler mathematical approach based on symmetry detection filters. Instead of using trained neural networks or complex algorithms that consume significant computational resources, the invention uses symmetry-based mathematical operations to detect objects, thereby reducing energy consumption while maintaining detection reliability
Solution Approach 2:
The invention uses simple, lightweight symmetry detection filters that can be quickly applied to images without requiring heavy computational models. These simple filters act as disposable, low-cost detection mechanisms that don't require the expensive training and processing associated with learning-based methods
2Measurement precision
If complex object detection algorithms are used to handle intra-class variations and illumination changes, then detection accuracy improves, but processing speed decreases
Solution Approach 1:
The patent exploits symmetry properties of objects and their motion patterns to create detection filters that are insensitive to illumination changes and pose variations. By detecting symmetric patterns in image differences, the system achieves high accuracy without requiring complex algorithms, thereby maintaining fast processing speeds
Solution Approach 2:
The invention changes the detection parameters from complex learned features to simple symmetry-based metrics. This parameter transformation allows the system to maintain high detection accuracy across different object classes and conditions while using computationally efficient operations that preserve processing speed
3Adaptability or versatility
If traditional object detection methods are used, then they can detect objects in specific conditions, but they fail to generalize across different object classes and viewing conditions
Solution Approach 1:
The patent creates universal symmetry detection filters that can detect multiple types of objects (people, vehicles, animals) across different classes and conditions using the same filter mechanism. This multi-functional approach eliminates the need for class-specific detectors and provides consistent reliability across diverse object categories and viewing conditions
Data Source
AI summary
A method and apparatus for detecting at least one of a location and a scale of an object in an image. The method comprising distinguishing the trailing and leading edges of a moving object in at least one portion of the image, applying a symmetry detection filter to at least a portion of the image to produce symmetry scores relating to the at least one portion of the image, and identifying at least one location corresponding to locally maximal symmetry scores of the symmetry scores relating to the at least one portion of the image, and utilizing the at least one location of the locally maximal symmetry scores to detect at least one of a location and a scale of the object in the image, wherein the scale relates to the size of the symmetry detection filter.