Vehicular Vision System Pedestrian Detection Fusion
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Solution Overview
Problem
Current vehicle vision systems face challenges with high-resolution image processing, which increases storage requirements, computational complexity, and hardware costs, limiting detection range and accuracy due to the need for larger bandwidth and more powerful processors.
Innovation Solution
The system employs a fusion of VGA and megapixel image processing, resizing high-resolution images to VGA resolution, and performing multiscale detection on cropped regions to reduce storage and computational demands while maintaining detection accuracy, allowing for efficient pedestrian detection and object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution image processing is used, then detection accuracy is improved, but storage requirements and computational complexity increase
Solution Approach 1:
The patent divides the high-resolution image into multiple lower-resolution sub-images or regions of interest. By segmenting the original image data, the system processes smaller portions separately, reducing the storage burden while maintaining detection accuracy through focused analysis of critical areas.
Solution Approach 2:
The patent extracts and processes only the most relevant regions or features from the high-resolution image rather than processing the entire image at full resolution. This extraction approach reduces storage requirements by eliminating redundant data while preserving the essential information needed for accurate pedestrian detection.
2Measurement precision
If high-resolution image processing is used, then detection accuracy is improved, but hardware costs increase
Solution Approach 1:
By segmenting the image processing task into multiple lower-resolution sub-tasks, the patent reduces the computational power and processing speed requirements of the hardware. This allows the use of less expensive processors and electronic circuitry while maintaining overall detection accuracy through the coordinated analysis of multiple sub-images.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image, focusing high-resolution analysis only on areas where pedestrians are likely to appear or where detection is most critical. This local quality approach reduces the overall computational burden and hardware requirements while maintaining detection accuracy in the most important areas.
3Measurement precision
If high-resolution image processing is used, then detection range is limited, but processing overhead increases
Solution Approach 1:
The patent segments the high-resolution image into multiple lower-resolution sub-images that can be processed in parallel or sequentially with reduced computational overhead. This segmentation enables the system to analyze larger areas or extend detection range while keeping processing time manageable by dividing the overall processing task into smaller, more efficient units.
Data Source
AI summary
A vision system for a vehicle includes a camera and an electronic control unit (ECU) with an image processor. The ECU generates a reduced resolution frame of captured image data and the ECU determines a reduced resolution detection result based on pedestrian detection using the reduced resolution frame of captured image data. The ECU, responsive to processing by the image processor of image data, generates a cropped frame of captured image data and the ECU determines a cropped detection result based on pedestrian detection using the cropped frame of captured image data. Responsive to determining the reduced resolution detection result and determining the cropped detection result, the ECU merges the reduced resolution detection result and the cropped detection result into a final pedestrian detection result. The final pedestrian detection result is indicative of presence of a pedestrian within the field of view of the camera.