Vehicle Gaze Detection Using Virtual Camera Space Training
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Solution Overview
Problem
Conventional approaches for training machine learning models for object identification require a significant amount of labeled training data, which is time-consuming and costly to create, especially in dynamic environments like vehicle interiors where camera positions can vary.
Innovation Solution
The use of a virtual camera space and a coordinate propagation mechanism to bridge the physical world with image data captured from arbitrary camera positions, allowing for the generation of ground truth data and training of neural networks across various vehicle configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional supervised training approaches are used to train machine learning models for object identification, then the model can achieve accurate object detection, but a significant amount of labeled training data is required which is time-consuming and costly to create
Solution Approach 1:
The patent uses image synthesis networks to generate synthetic training images that copy the visual characteristics and patterns of real images. These synthesized images serve as training data substitutes, eliminating the need to manually create large amounts of labeled real images while maintaining the statistical properties needed for accurate model training
Solution Approach 2:
The system performs self-service by automatically generating its own training data through the image synthesis network. Instead of requiring external manual annotation efforts, the model can be trained using synthetically generated images that are created algorithmically, making the training process self-sufficient and eliminating the bottleneck of manual data preparation
2Measurement precision
If conventional supervised training approaches are used to train machine learning models, then the model can achieve accurate object detection, but the process becomes too expensive for various uses
Solution Approach 1:
The patent uses image synthesis networks to generate synthetic training images that copy the visual characteristics and patterns of real images. These synthesized images serve as training data substitutes, eliminating the need to manually create large amounts of labeled real images while maintaining the statistical properties needed for accurate model training
Solution Approach 2:
The system replaces expensive, time-intensive manual data annotation processes with inexpensive automated synthetic image generation. The synthesized images act as disposable training data that can be generated on-demand without the recurring costs of human annotators, making the training process economically viable
3Measurement precision
If conventional supervised training approaches are used to train machine learning models, then the model can achieve accurate object detection, but an insufficient amount of training data may result
Solution Approach 1:
The patent uses image synthesis networks to generate synthetic training images that copy the visual characteristics and patterns of real images. These synthesized images serve as training data substitutes, eliminating the need to manually create large amounts of labeled real images while maintaining the statistical properties needed for accurate model training
Solution Approach 2:
The system performs preliminary action by pre-generating large quantities of synthetic training images before the actual model training begins. This advance preparation ensures that sufficient training data is available, eliminating the constraint of limited real labeled images and enabling comprehensive model training
Data Source
AI summary
Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.