Adaptive Template Object Classification for Collision Warning
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Solution Overview
Problem
Current collision warning and countermeasure systems face inefficiencies in object detection and classification due to high processing power requirements and impracticality of generating complete template sets for all possible objects, leading to potential false object detection and increased computational burdens.
Innovation Solution
A system that combines data from electro-magnetic and electro-optical sensors to generate and adapt object classification templates, minimizing image processing time and complexity by using radar and camera sensors to create candidate templates and validate them for efficient object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template matching is used for object classification, then object classification capability is improved, but processing time and computational power requirements increase significantly
Solution Approach 1:
The patent segments the image processing task by dividing the image into multiple regions of interest (ROIs) based on radar detection data. Instead of processing the entire high-resolution image, only specific regions containing potential objects are extracted and processed, significantly reducing computation time while maintaining classification accuracy.
Solution Approach 2:
The system performs preliminary object detection using radar sensors before applying template matching with the camera. This preliminary action identifies potential objects and their locations, allowing the subsequent template matching process to focus only on relevant regions rather than processing the entire image frame.
2Adaptability or versatility
If complete template sets are generated for all possible objects, then object classification coverage is improved, but memory requirements and system complexity increase
Solution Approach 1:
The patent implements a dynamic template generation system that creates templates adaptively based on detected objects rather than storing all possible templates in advance. When new object types are detected, the system generates appropriate templates on-demand, allowing the template set to evolve dynamically with usage patterns while keeping memory requirements manageable.
Solution Approach 2:
The system serves itself by automatically generating templates from detected objects without requiring pre-existing complete template libraries. The template generation capability is built into the system, allowing it to create appropriate templates autonomously when encountering new object types during operation.
3Measurement precision
If high-resolution image data is processed, then object detection accuracy is improved, but processing power requirements and cost increase
Solution Approach 1:
The patent segments the high-resolution image into multiple lower-resolution regions of interest based on radar detection data. By processing only the necessary regions at reduced resolution, the system maintains adequate detection accuracy while significantly reducing the processing power required compared to processing the entire high-resolution image.
Solution Approach 2:
The system applies partial processing by focusing computational resources only on regions of interest identified by radar, rather than processing the entire image. This partial action approach provides sufficient detection accuracy for safety-critical applications while avoiding the excessive processing power requirements of complete image processing.
Data Source
AI summary
A method of performing object classification within a collision warning and countermeasure system (10) of a vehicle (12) includes the generation of an object detection signal in response to the detection of an object (52). An image detection signal is generated and includes an image representation of the object (52). The object detection signal is projected on an image plane (51) in response to the image detection signal to generate a fused image reference. A candidate template (55) is generated in response to the fused image reference. The candidate template (55) is validated. The object (52) is classified in response to the candidate template (55).