Landmark Detection for Environment Dimensions and Object Localization
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Solution Overview
Problem
Existing computer vision systems fail to accurately determine the location of objects relative to the environment in images, as they primarily rely on two-dimensional pixel coordinates, which are insufficient for understanding the object's position in the real-world environment.
Innovation Solution
A neural network is trained with a dataset of environment images featuring labeled landmarks, enabling it to identify landmarks in input images, calculate homography matrices, and convert pixel coordinates to physical coordinates using a pre-built environment model, allowing for the visualization of landmarks and objects in the environment plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel coordinates are used to describe object location, then the location can be precisely identified in the image, but the location cannot be meaningfully interpreted relative to the real-world environment
Solution Approach 1:
The patent introduces landmarks as intermediary elements that bridge the gap between pixel coordinates and real-world environment coordinates. Landmarks are detected in the image and matched with pre-stored environment models to establish a coordinate transformation relationship, enabling conversion from image space to environment space while preserving both precision and contextual meaning
2Measurement precision
If landmark detection and homography matrix calculation are implemented, then environment dimensions and object locations can be accurately determined, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-storing environment models with known landmark positions and dimensions before actual object detection occurs. This pre-computation of environment geometry allows the system to quickly transform detected landmark positions into meaningful environment coordinates without performing complex calculations in real-time, thus reducing system complexity during operation
Solution Approach 2:
The homography matrix serves as an intermediary computational tool that simplifies the coordinate transformation process. Instead of directly mapping complex pixel coordinates to environment dimensions, the homography matrix provides a standardized transformation framework that handles perspective distortions and enables consistent coordinate conversion across different viewpoints
Data Source
AI summary
Disclosed herein are systems and method for determining environment dimensions based on landmark detection, the method including: training, with a dataset including a plurality of images featuring an environment and labelled landmarks in the environment, a neural network to identify the labelled landmarks in an arbitrary image of the environment; receiving an input image depicting the environment; generating an input tensor based on the received input image; inputting the input tensor into the neural network, wherein the neural network is configured to generate an output tensor including a position of each identified landmark and a visibility score associated with each position; calculating a homography matrix between each position in the output tensor along a camera plane and a corresponding position in an environment plane, based on a pre-built model of the environment; and outputting an image that visually connects each landmark along the environment plane based on the homography matrix.