Vanishing Point and Symmetry Refinement for Fast Vehicle Detection
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Solution Overview
Problem
Existing object detection algorithms in autonomous systems, such as ADAS and AD, require significant computing resources and generate results slower than desired, particularly in automotive applications.
Innovation Solution
Implement techniques for vanishing point determination, symmetry-based boundary refinement, and component detection using less-complex methods that leverage image processing to identify vanishing points, refine object boundaries based on symmetry, and detect specific components without prior knowledge of vehicle make/type, reducing computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex computer vision algorithms or deep neural networks are used for object detection, then detection accuracy is improved, but computing resource consumption increases and processing speed decreases
Solution Approach 1:
The patent segments the object detection process into distinct stages: vanishing point determination from line segments, boundary identification, symmetry-based refinement, and component detection. Each stage processes specific features independently, avoiding the need for resource-intensive end-to-end deep learning while maintaining accuracy through structured multi-step analysis
Solution Approach 2:
The patent extracts and utilizes specific geometric features (vanishing points, line segments, symmetry axes) from images to guide the detection process. By extracting these key geometric properties and using them as priors, the system avoids processing entire images through complex neural networks, significantly reducing computational load while preserving detection accuracy
2Measurement precision
If complex computer vision algorithms or deep neural networks are used for object detection, then detection accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The detection pipeline is divided into computationally lightweight stages: line segment extraction, vanishing point calculation, boundary identification, and symmetry-based refinement. Each stage processes only relevant features with simple algorithms, avoiding the high energy consumption of training and running deep neural networks while achieving comparable accuracy
Solution Approach 2:
The patent extracts essential geometric information (vanishing points, symmetry axes) that serve as strong priors for object detection. By relying on these extracted geometric features rather than learning from large datasets through energy-intensive neural networks, the system achieves accurate detection with minimal computing resources
Data Source
AI summary
A method includes obtaining, using at least one processing device, image data associated with a scene. The method also includes identifying, using the at least one processing device, multiple line segments based on the image data. The method further includes identifying, using the at least one processing device, one or more boundaries around one or more objects detected in the image data. In addition, the method includes estimating, using the at least one processing device, a position of a vanishing point associated with the image data based on multiple collections of the line segments while excluding, from the multiple collections, one or more of the line segments that overlap with or that are included within the one or more boundaries.