Neural Network Weight Rotation for Tilted Camera Feature Analysis
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Solution Overview
Problem
XR devices with tilted cameras produce inaccurate feature analysis outputs due to misalignment between training images and captured images, necessitating costly preprocessing or extensive new data collection.
Innovation Solution
Rotate network weights after training on horizon-aligned images to align with tilted camera orientations, allowing deployment on XR devices without image preprocessing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image preprocessing (rotation) is performed to align tilted camera images with training data, then feature analysis accuracy is improved, but computational cost and device complexity increase
Solution Approach 1:
Instead of rotating the input images to match the training data orientation, the patent inverts the approach by rotating the neural network weights to match the tilted camera orientation. This is achieved by applying a rotation transformation to the weight matrices that corresponds to the camera's tilt angle, allowing the network to directly process tilted images without preprocessing.
Solution Approach 2:
The weight rotation is performed as a preliminary step during model deployment or loading, before the actual inference process. By pre-rotating the weights according to the known camera orientation, the system eliminates the need for runtime image rotation operations, reducing computational overhead during actual feature analysis.
2Measurement precision
If image preprocessing (rotation) is performed to align tilted camera images with training data, then feature analysis accuracy is improved, but processing time increases
Solution Approach 1:
The weight rotation is performed as a preliminary step during model deployment or loading, before the actual inference process. By pre-rotating the weights according to the known camera orientation, the system eliminates the need for runtime image rotation operations, reducing computational overhead during actual feature analysis.
Solution Approach 2:
The patent extracts and removes the image rotation operation from the inference pipeline by incorporating the rotation transformation directly into the neural network weights. This separation eliminates the preprocessing step entirely, allowing the network to work directly with tilted camera images.
3Measurement precision
If extensive new training data is collected for tilted camera orientations, then feature analysis accuracy is improved, but data collection cost and time increase
Solution Approach 1:
The patent changes the parameter space by transforming the weight matrices through a rotation operation rather than changing the training data. This mathematical transformation allows the existing horizontally-aligned training data to be effectively adapted to tilted camera orientations without requiring new data collection.
Solution Approach 2:
The approach creates a transformed copy of the existing neural network weights through rotation, rather than training a new network from scratch or collecting new data. This weight rotation copy maintains the learned features from the original training data while adapting them to the tilted orientation.
Data Source
AI summary
Disclosed are systems and techniques for network parameter (e.g., weight) rotation for oriented camera images. For example, a computing device can obtain an image of a scene. The image is vertically aligned with the scene. The computing device can process the image using a neural network with rotated weights. The neural network is trained using a plurality of training images to generate a plurality of weights. The plurality of training images are horizontally aligned with a reference scene. The plurality of weights are rotated to generate the rotated weights.