Monocular Fisheye Rider Awareness for Low-Complexity Object Detection
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Solution Overview
Problem
Existing systems for providing environmental awareness to vehicle operators, such as cyclists and drivers, are costly, computationally expensive, and require specialized sensors, making them impractical for low-cost implementations and vehicles with varying motion profiles.
Innovation Solution
A system utilizing monocular fisheye cameras and motion/orientation sensors, coupled with intelligent processing, to detect and characterize surrounding objects without depth information, and provide selective alerts, optimizing for computational efficiency and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized sensors (e.g., LiDAR, depth cameras) are used to detect surrounding objects, then object detection accuracy is improved, but system cost and device complexity increase significantly
Solution Approach 1:
The patent uses monocular fisheye cameras to capture images that serve as 2D projections of the 3D environment. Instead of directly using expensive depth sensors, the system creates computational models (depth maps, 3D point clouds) by processing these 2D images through algorithms that infer depth information from perspective cues, occlusions, and motion parallax.
Solution Approach 2:
The patent replaces mechanical/optical depth sensing systems (LiDAR, stereo cameras) with a computational approach using monocular vision. The system substitutes physical depth-measuring mechanisms with image processing algorithms that derive depth information from single-viewpoint 2D images through computer vision techniques.
2Loss of information
If multiple high-resolution cameras are used to provide 360-degree coverage, then environmental awareness is improved, but computational expense and processing requirements increase
Solution Approach 1:
The patent divides the environment into multiple zones (front, rear, left, right) and uses a single fisheye camera per zone rather than multiple cameras covering the entire 360 degrees. This segmentation allows the system to process fewer images at lower resolution while still achieving comprehensive coverage through strategic camera placement and field-of-view optimization.
Solution Approach 2:
The patent uses fisheye lenses with extremely wide fields of view (180 degrees or more) that capture more information than necessary in each direction. This excessive coverage in each zone compensates for using fewer cameras, allowing the system to achieve 360-degree awareness with minimal camera count and reduced processing load.
3Measurement precision
If depth information is collected using specialized sensors, then object distance measurement accuracy is improved, but system cost increases
Solution Approach 1:
The patent introduces computational algorithms as an intermediary between the monocular camera and the depth information. Instead of directly measuring distance with specialized sensors, the system uses image processing techniques (perspective projection geometry, object size comparison, motion parallax) to infer depth as an intermediate step that enables accurate distance measurement.
Solution Approach 2:
The patent changes the approach from direct physical measurement of depth to computational derivation. By transforming the problem from measuring a physical parameter (depth) directly to calculating it from 2D image parameters (pixel coordinates, focal length, perspective distortion), the system achieves accurate distance measurement without specialized depth sensors.
4Measurement precision
If conventional cameras are used instead of fisheye cameras, then image quality is improved, but field of view coverage decreases
Solution Approach 1:
The patent transitions from the conventional dimension of image quality (sharpness, resolution) to a different dimension of performance (field of view coverage). By accepting controlled distortion and lower per-pixel quality in exchange for 180-degree or wider coverage, the system optimizes for the dimension that matters most in environmental awareness applications.
Solution Approach 2:
The patent changes the optical parameters of the camera system by using fisheye lenses with extreme curvature and wide aperture angles. This parameter change fundamentally alters the projection geometry from rectilinear to equidistant or stereographic projection, enabling ultra-wide coverage while managing distortion through computational correction rather than optical perfection.
Data Source
AI summary
A method for providing a rider with a dynamic environmental awareness includes: receiving data from a set of sensors; correcting data; identifying a set of regions in the data; and processing the set of regions to detect a set of objects. In variants, the method can additionally include stabilizing the objects detected from the set of regions.