Automotive 3D Head Pose Prediction with Personalized Models
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Solution Overview
Problem
Existing head pose inference technologies face challenges in accurately collecting ground truth 3D pose data, which is crucial for training machine learning models to predict head poses in vehicle occupants. Current methods, such as using inertial measurement sensors or depth perception data from stereo vision and time-of-flight cameras, suffer from inaccuracies and high costs.
Innovation Solution
A system that uses a personalized optimized head model of a vehicle occupant, generated through a registration process involving a depth sensor, to collect accurate ground truth head pose data. This model is optimized to reflect the unique characteristics of the training subject, allowing for precise head pose prediction without the need for depth data in the input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inertial measurement sensors or depth perception data from stereo vision and time-of-flight cameras are used to collect ground truth 3D pose data, then head pose measurement can be obtained, but measurement precision deteriorates and device complexity increases
Solution Approach 1:
The patent creates a personalized 3D head model that copies and represents the actual head geometry of the training subject. This virtual model serves as a simplified substitute for complex sensor systems, enabling accurate pose estimation through geometric registration rather than requiring multiple depth sensors or inertial measurement units.
Solution Approach 2:
The patent replaces mechanical sensor systems (depth sensors, stereo cameras, inertial measurement sensors) with a computational approach using 2D image data and 3D model registration. The mechanical depth sensing system is substituted with an algorithmic system that infers 3D pose from 2D images through geometric transformation and model matching.
2Reliability
If depth sensors are used to capture 3D point cloud data for training, then ground truth head pose data can be collected, but device complexity and cost increase
Solution Approach 1:
The patent creates a personalized 3D head model that copies and represents the actual head geometry of the training subject. This virtual model serves as a simplified substitute for complex sensor systems, enabling accurate pose estimation through geometric registration rather than requiring multiple depth sensors or inertial measurement units.
Solution Approach 2:
The patent introduces a personalized 3D head model as an intermediary between the 2D image data and the ground truth pose labels. This model acts as a mediator that enables the transformation from 2D image coordinates to 3D pose information without requiring direct depth sensing during data collection.
3Device complexity
If a generic head model is used for training, then device complexity is reduced, but measurement precision deteriorates due to lack of personalized characteristics
Solution Approach 1:
The patent applies local quality by creating a personalized head model that captures the specific geometric characteristics of each training subject. Instead of using a uniform generic model for all subjects, the system adapts the model to reflect individual variations in head shape, size, and facial features, thereby improving measurement precision for each person.
Solution Approach 2:
The patent performs preliminary action by collecting 3D point cloud data during a registration phase to create the personalized head model before the actual training data collection. This pre-established personalized model is then used throughout training to ensure accurate pose estimation without requiring complex sensors during the main data collection process.
Data Source
AI summary
In various examples, head pose prediction for automotive occupant sensing systems and applications is presented. The systems and methods described herein provide for a machine learning model trained using a dataset that comprises ground truth head pose data computed using a registered head model of a training subject. While operating a vehicle, one or more cameras and a depth sensor capture synchronized images of the training subject. To compute a ground truth 3D head pose, angular deviations between a 3D point cloud and the registered head model may be computed to obtain a 3D ground truth head pose measurement. Using an extrinsic calibration transform, the head pose measurement may be mapped into the sensor coordinate frame. Training samples may be produced for training the machine learning model that comprise an optical image frame and the head pose measurement transposed into the frame of reference for that optical image frame.