Neural Network Pose Estimation for Perspective-Resilient Environment Dimensions
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Solution Overview
Problem
Existing methods for determining environment dimensions, such as key point detection, are inaccurate due to camera perspective distortions, making it difficult to accurately locate objects in environments like sports fields relative to their actual dimensions.
Innovation Solution
A method and system using a neural network trained with images featuring labeled landmarks, which generates a homography matrix to accurately determine environment dimensions by identifying the pose of the environment in an input image, even when the camera perspective does not match the training data, and outputs an image connecting landmarks on the environment plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If key point detection is used to determine environment dimensions, then the process is simple, but the measurement precision deteriorates due to camera perspective distortions
Solution Approach 1:
The patent introduces a neural network as an intermediary between the input image and the environment dimension determination. The neural network processes the image data and outputs refined landmark positions that are resistant to perspective distortions, thereby improving measurement precision without significantly complicating the overall process
Solution Approach 2:
The patent transforms the input image into a tensor representation and processes it through the neural network to output transformed coordinates that represent environment plane positions. This parameter transformation from pixel coordinates to environment coordinates resolves the perspective distortion issue while maintaining computational efficiency
2Device complexity
If traditional key point detection is used, then the device complexity is low, but the reliability of dimension determination worsens under varying camera perspectives
Solution Approach 1:
The neural network is trained on diverse images with various camera perspectives and is designed to universally handle different viewing angles. The network outputs transformed coordinates that are valid across multiple perspectives, making the system reliable without requiring separate processing for each camera angle
Solution Approach 2:
The patent transitions from working in the image plane coordinate system to the environment plane coordinate system through the neural network's coordinate transformation. This dimensional transformation allows the system to determine reliable environment dimensions independent of camera perspective
3Productivity
If real-time processing is implemented for video streams, then the productivity increases, but the measurement precision may deteriorate due to processing constraints
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with a neural network-based system. The neural network processes images in an optimized computational manner that achieves both real-time performance and high precision landmark detection, overcoming the trade-off between speed and accuracy
Data Source
AI summary
Disclosed herein are systems and method for determining environment dimensions based on environment pose. In one aspect, the method may include training, with a dataset including a plurality of images featuring an environment and labelled landmarks in the environment, a neural network to identify a pose of an environment. The method may comprise receiving an input image depicting the environment, generating an input tensor based on the input image, and inputting the input tensor into the neural network, which may be configured to generate an output tensor including a position of each identified landmark, a confidence level associated with each position, and a pose confidence score. The method may include calculating a homography matrix between each position in the output tensor along a camera plane and a corresponding position in an environment plane in order to output an image that visually connects each landmark along the environment plane.