Depth-Map Vehicle Boundary Detection for Heavy-Duty Recognition
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Solution Overview
Problem
Existing vehicle recognition technologies using pattern matching or machine learning struggle to recognize heavy-duty vehicles due to their large size and varying cargo, which can exceed image boundaries or be partially hidden.
Innovation Solution
An image processing device and method that utilizes depth map information to identify vehicle areas by recognizing area classes and determining range information discontinuities, allowing for the detection of vehicles that cannot be recognized through traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern matching or machine learning is used for vehicle recognition, then recognition speed and accuracy for standard vehicles is improved, but recognition capability for heavy-duty vehicles exceeds image boundaries or carrying various kinds of cargo fails
Solution Approach 1:
The patent transitions from two-dimensional image data to three-dimensional depth map information. By utilizing depth data from multiple cameras, the system creates a 3D representation of the scene, enabling recognition of heavy-duty vehicles that extend beyond image boundaries or are partially occluded, thus resolving the limitation of traditional 2D pattern matching methods.
Solution Approach 2:
The patent segments the vehicle recognition problem into multiple components: depth map generation from multiple cameras, area class recognition for different regions, and boundary detection through range information analysis. This segmentation allows the system to handle complex heavy-duty vehicle scenarios by processing depth data and range discontinuities separately from traditional image pattern recognition.
2Device complexity
If traditional image-based methods are used, then processing complexity is reduced, but recognition fails for vehicles too large to fit within images
Solution Approach 1:
The system adds the depth dimension to traditional image processing by generating depth maps from multiple camera views. This 3D information enables reliable detection of heavy-duty vehicles that span beyond single image boundaries, improving recognition reliability without excessively increasing processing complexity through efficient depth map generation and range information analysis.
3Measurement precision
If depth map information is utilized for vehicle detection, then recognition accuracy for occluded and large vehicles is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex depth-based recognition task into manageable segments: generating depth maps from multiple cameras, classifying area types in depth space, extracting range information for vehicle areas, and detecting boundaries through range discontinuities. This segmentation reduces overall processing complexity by breaking down the 3D recognition problem into sequential, optimized sub-tasks.
Data Source
AI summary
Among multiple different area classes designated regarding photographic subjects, to which area class a photographic subject of each pixel in a captured image that has been acquired belongs is recognized. From depth map information corresponding to the captured image, range information of each pixel in an area in the captured image representing a vehicle area class among the multiple different area classes is acquired, and a position at which the range information is discontinuous is determined to be a boundary between different vehicles.