Rear Obstruction Detection Using Image Differencing
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Solution Overview
Problem
Current rear-view camera systems for vehicles do not provide analysis or warnings for obstacles behind the vehicle, which are common causes of accidents, especially when reversing, as they lack image processing capabilities to detect and classify obstructing objects effectively.
Innovation Solution
A rear-view camera system with a processor that captures and processes image frames from the immediate vicinity of the vehicle, detecting multiple classes of obstructing objects, such as spherical balls, vertical poles, and hanging chains, using image differencing and illumination techniques to provide real-time warnings to the driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a rear-view camera system is installed to provide additional visibility, then the driver can see obstacles behind the vehicle, but the system does not provide automatic detection or warning capabilities
Solution Approach 1:
The patent introduces an intermediary processing system that includes a processor connected to the camera that automatically analyzes captured images, detects obstacles, and generates warnings. This intermediary layer transforms the simple camera system into an intelligent detection system without requiring the driver to manually analyze every image frame.
Solution Approach 2:
The patent replaces manual visual inspection by the driver with automated image processing and obstacle detection algorithms. The processor automatically analyzes the camera feed, identifies potential obstacles, and triggers warnings, substituting human cognitive processing with computational analysis.
2Measurement precision
If image processing is added to detect and classify obstacles, then obstacle detection capability is improved, but processing time and computational requirements increase
Solution Approach 1:
The patent segments the image processing task into distinct functional modules: image capture, preprocessing, obstacle detection, classification, and warning generation. This segmentation allows each module to be optimized independently and enables parallel processing of different image regions or features, reducing overall processing time.
Solution Approach 2:
The system performs partial processing by focusing computational resources on detecting only the most critical obstacle types and features relevant to immediate safety, rather than analyzing every detail of the image. This selective approach maintains detection accuracy for safety-critical objects while reducing unnecessary computational overhead.
3Adaptability or versatility
If the system processes multiple classes of objects in a single frame, then detection coverage is improved, but processing complexity increases
Solution Approach 1:
The patent implements a universal detection framework that can identify multiple classes of obstacles (pedestrians, vehicles, animals, other objects) using a single integrated processing pipeline. The system uses general-purpose image processing techniques combined with class-specific feature detection, allowing one system to perform multiple detection functions without requiring separate dedicated systems for each object type.
Data Source
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AI summary
A method is provided using a system mounted in a vehicle. The system includes a rear-viewing camera and a processor attached to the rear-viewing camera. When the driver shifts the vehicle into reverse gear, and while the vehicle is still stationary, image frames from the immediate vicinity behind the vehicle are captured. The immediate vicinity behind the vehicle is in a field of view of the rear-viewi ng camera. The image frames are processed and thereby the object is detected which if present in the immediate vicinity behind the vehicle would obstruct the motion of the vehicle. The processing is preferably performed in parallel for a plurality of classes of obstructing objects using a single image frame of the image frames.