Trailer Coupler Recognition Using Descriptor Clusters on Low-End Processors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision systems for detecting trailers and trailer couplers in images require expensive graphic processing units (GPUs) or high-end central processing units (CPUs), making them costly and inefficient for widespread use.
Innovation Solution
A method and system that generate a database of descriptor clusters using training image data, where descriptors are matched to identify trailers or trailer couplers in images, utilizing low-end processing architecture without GPUs or high-end CPUs, by determining features and descriptors and generating a convex hull representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPUs or high-end CPUs are used for trailer detection, then detection accuracy and reliability are improved, but system cost and complexity increase
Solution Approach 1:
The patent segments the trailer detection task into distinct processing stages: feature extraction, descriptor generation, descriptor matching against database clusters, and convex hull generation. Each stage operates with specific data structures and algorithms, allowing the system to achieve reliable detection through coordinated simple operations rather than requiring a single complex high-end processor
Solution Approach 2:
The system performs preliminary action by pre-processing training images to generate a database of descriptor clusters before actual detection occurs. This offline preparation creates reusable reference data that simplifies runtime processing, enabling accurate trailer identification through matching against pre-computed clusters rather than requiring complex real-time analysis
2Measurement precision
If GPUs or high-end CPUs are used for trailer detection, then detection precision is improved, but system cost increases
Solution Approach 1:
The patent employs cheap short-living objects by using lightweight, computationally inexpensive algorithms and data structures that can be executed on low-end processors. The descriptor matching process uses simple comparison operations against pre-computed clusters, and the convex hull algorithm generates detection results through efficient geometric computations, all designed to run on budget-friendly hardware without requiring expensive GPUs or high-end CPUs
Solution Approach 2:
The system applies parameter changes by transforming images through scale-space extrema detection and generating invariant descriptors that are robust to scale, rotation, and illumination changes. These parameter transformations enable precise trailer detection through mathematical invariance properties rather than requiring complex hardware-based image processing capabilities
3Reliability
If complex processing algorithms are used for trailer detection, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing training images to generate a database of descriptor clusters before actual detection occurs. This offline preparation creates reusable reference data that simplifies runtime processing, enabling accurate trailer identification through matching against pre-computed clusters rather than requiring complex real-time analysis
Solution Approach 2:
The patent segments the trailer detection task into distinct processing stages: feature extraction, descriptor generation, descriptor matching against database clusters, and convex hull generation. Each stage operates with specific data structures and algorithms, allowing the system to achieve reliable detection through coordinated simple operations rather than requiring a single complex high-end processor
Data Source
AI summary
A method and system are disclosed for identifying a trailer or trailer coupler in an image. The method includes obtaining a database of descriptor clusters. Each descriptor cluster has at least one label assigned thereto. Each at least one label is a label for a trailer or trailer coupler, or for a background. Image data pertaining to an image is received. Features and descriptors are determined in the received image data. For each determined descriptor, the method includes matching the determined descriptor with a descriptor cluster in the database and assigning the label corresponding to the matched descriptor cluster to the determined descriptor. Based upon the determined descriptors having the assigned label corresponding to one of a trailer or a trailer coupler, the method includes determining a convex hull of a representation of the one of the trailer or trailer coupler in the image.