Single Camera Obstacle Detection via Pixel Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting obstacles using cameras, such as stereoscopic cameras, radar, and LIDAR, are costly and complex, and lack efficient solutions for automatic collision avoidance with a single moving camera.
Innovation Solution
A camera system that captures and processes images at different times to identify pixel coordinates and detect obstacles by matching pixels between images, using a digital signal processor to output signals for collision avoidance, either through audible warnings or vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stereoscopic camera equipment, radar equipment, and/or LIDAR equipment are used for detecting objects, then the detection accuracy and reliability are improved, but the cost and device complexity increase significantly
Solution Approach 1:
The patent segments the detection task into two parts: using a single camera for general obstacle detection and a separate machine learning classification system for identifying object types. This segmentation allows the system to achieve reliable detection without the complexity of stereoscopic cameras, radar, or LIDAR equipment.
Solution Approach 2:
The patent introduces machine learning as an intermediary component that processes the images captured by the single camera. This intermediary enables the system to achieve detection reliability comparable to complex systems by adding intelligent analysis capabilities to the simple camera setup.
2Measurement precision
If stereoscopic camera equipment, radar equipment, and/or LIDAR equipment are used for detecting objects, then the detection accuracy is improved, but the cost increases significantly
Solution Approach 1:
The patent replaces expensive, complex detection equipment (stereoscopic cameras, radar, LIDAR) with a single inexpensive camera. The system compensates for the camera's limitations through software-based image processing and machine learning, achieving accurate detection at a fraction of the cost of traditional systems.
Solution Approach 2:
The patent substitutes mechanical/optical systems (stereoscopic cameras, radar antennas, LIDAR scanners) with a computational approach using a single camera combined with digital image processing and machine learning algorithms. This replacement eliminates the need for complex hardware while maintaining or improving detection accuracy.
3Adaptability or versatility
If object classification by machine learning is used, then the detection capability is improved, but the system requires prior learning phases and increased processing complexity
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning model offline before deployment. The system learns object characteristics during a training phase using labeled data, then applies this learned knowledge during actual operation. This preliminary action eliminates the need for real-time learning phases while maintaining high adaptability.
Solution Approach 2:
The patent implements a dynamic system where the machine learning model can be retrained and updated with new data over time. This dynamic capability allows the system to adapt to new object types and changing environments without requiring complete system redesign, balancing versatility with manageable processing complexity.
Data Source
AI summary
For detecting an obstacle with a camera, a first image is viewed by the camera at a first location during a first time. Points on a surface would project onto first pixels of the first image. A second image is viewed by the camera at a second location during a second time. The points on the surface would project onto second pixels of the second image. Coordinates of the second pixels are identified in response to coordinates of the first pixels, in response to a displacement between the first and second locations, and in response to a distance between the camera and the surface. The obstacle is detected in response to whether the first pixels substantially match the second pixels.