Adaptive Weighted Grid for Driver Assistance Obstruction Detection
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Solution Overview
Problem
Existing camera systems for driver assistance lack efficient methods to dynamically adjust the region of interest in image frames, particularly in scenarios with minimal image contrast and ego-motion, leading to suboptimal obstruction detection and road environment analysis.
Innovation Solution
A camera system with a pre-processing circuit that adapts weights on a low-resolution grid to define a region of interest, allowing for flexible adjustment of the region's position and shape based on image parameters like intensity and motion, enabling efficient processing and parameter resetting for improved driver assistance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a low-resolution grid with adaptive weights is used to define region of interest, then processing efficiency is improved, but measurement precision of image parameters deteriorates
Solution Approach 1:
The image sensor output is segmented into multiple cells that are grouped into a reduced-resolution grid. Each cell represents multiple contiguous picture elements, creating a hierarchical structure that enables efficient processing while preserving essential image information for region of interest definition.
Solution Approach 2:
Different weights are assigned to different cells in the grid based on local image characteristics such as intensity variations, color intensity, and gradient. This local quality approach allows the system to adaptively emphasize important regions while maintaining processing efficiency through the reduced grid resolution.
2Adaptability or versatility
If weights are adaptively changed responsive to scene changes, then adaptability is improved, but device complexity increases
Solution Approach 1:
The weights assigned to cells are dynamically adjusted based on real-time image parameters including intensity, color intensity, and gradient. This dynamic adaptation allows the region of interest to automatically respond to scene changes without requiring complex manual configuration or reconfiguration of the entire processing system.
Solution Approach 2:
The system changes physical parameters (weights) in response to measured image characteristics. By monitoring intensity, color intensity, and gradient parameters and adjusting weights accordingly, the system achieves adaptability through parameter changes rather than structural modifications, managing complexity effectively.
3Device complexity
If multiple picture elements are grouped into cells, then device complexity is reduced, but measurement precision of image details deteriorates
Solution Approach 1:
Multiple contiguous picture elements are segmented and grouped into cells, which are then organized into a reduced-resolution grid. This segmentation hierarchy reduces the number of individual processing units from the full pixel count to a manageable number of cells, simplifying the processing circuit while maintaining sufficient detail for region of interest determination.
Solution Approach 2:
The system performs full-resolution processing only where necessary (in the defined region of interest) while using reduced-resolution processing for the entire image. This partial action approach maintains image detail precision where needed while reducing overall device complexity through selective processing resolution.
Data Source
AI summary
A camera system including: an image sensor that is controlled by one or more camera parameters; and a pre-processing circuit adapted to associate weights respectively with cells of a grid, wherein the weights differ from the one or more camera parameters, wherein the cells respectively include a plurality of contiguous picture elements of the image sensor.