Vehicle Head Pose Tracking Using Seat-Based 3D Outlier Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing head-pose tracking systems are unreliable and inefficient in dynamic environments like moving vehicles, due to issues with depth data measurements under varying lighting conditions, leading to high latency and error in tracking.
Innovation Solution
A system and method that refine head pose tracking by utilizing seat position and orientation data to determine a three-dimensional volume where the user's head is likely to be, thereby discarding outlier positions and improving tracking accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If depth cameras are used for head pose tracking, then tracking coverage is improved, but measurement precision deteriorates under varying lighting conditions
Solution Approach 1:
The patent introduces an intermediary computational process that fuses data from multiple sensors (depth camera, visible-light camera, inertial sensor) and uses machine learning algorithms to mediate between the conflicting requirements. The system processes raw data from each sensor type through separate processing pipelines and combines them to produce a reliable head pose estimate, allowing the depth camera to operate across varying lighting conditions without sacrificing measurement precision.
Solution Approach 2:
The system dynamically adjusts processing parameters based on environmental conditions. When lighting conditions degrade depth camera performance, the system increases reliance on visible-light camera data and inertial sensor fusion, adjusting the weighting parameters in the multi-sensor fusion algorithm to maintain measurement precision while preserving tracking coverage.
2Reliability
If multiple tracking cameras are used to improve tracking reliability, then device complexity increases
Solution Approach 1:
The patent makes each camera serve multiple functions: the depth camera not only tracks head pose but also provides spatial context and distance information, while the visible-light camera serves both as a backup for depth measurement and as a primary source for texture and color information. The inertial sensor serves dual purposes of direct pose measurement and motion compensation for camera data, reducing the need for additional dedicated sensors.
Solution Approach 2:
The system replaces the mechanical approach of using multiple physical cameras with a computational approach using sensor fusion and machine learning. Instead of physically installing multiple cameras to achieve reliability, the system uses algorithms to fuse data from fewer sensors, achieving comparable or superior reliability with reduced hardware complexity.
3Speed
If computer vision algorithms process continuous image streams, then tracking responsiveness is improved, but loss of time increases due to computational load
Solution Approach 1:
The system implements periodic processing at optimized intervals rather than continuous frame-by-frame processing. The multi-sensor fusion algorithm processes data at the least common multiple of sensor update rates, and uses temporal smoothing to maintain responsive tracking between processing cycles. This reduces computational load while preserving perceived responsiveness through predictive algorithms that estimate head pose between processing intervals.
Solution Approach 2:
The system performs preliminary processing of sensor data in real-time as it arrives, pre-computing features and intermediate results that will be needed for head pose estimation. By preparing data structures and pre-processing images before the main computational step, the system reduces the actual processing time required for each tracking update, thereby reducing latency while maintaining responsiveness.
Data Source
AI summary
Tracking means is utilised to estimate potential positions of a head of a user sitting on a seat of a vehicle. A three-dimensional (3D) volume where the user's head is likely to be present is determined, based on a current setting of adjustable seat parameter(s), the adjustable seat parameter(s) being detected by sensor(s). It is detected whether at least one of the plurality of potential positions of the head lies outside the 3D volume. When it is detected that at least one of the plurality of potential positions of the head lies outside the 3D volume, the at least one of the plurality of potential positions is considered as an outlier, and a correct position of the user's head is determined as one of the plurality of potential positions of the head that lies within the 3D volume.