Probabilistic Graphical Model for Gaze Tracking Error Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current gaze tracking systems suffer from inaccuracies due to noise in eye-tracker data, head movements, and other environmental factors, making it difficult to reliably use gaze information for object tracking and interaction.
Innovation Solution
A probabilistic-based process is employed to predict user fixation patterns in displayed scenes by combining eye-tracker data with prior knowledge about the scene, using a probabilistic graphical model to improve the accuracy and robustness of gaze-contingent interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If gaze tracking data is used directly for object tracking, then the system responds quickly to user input, but the tracking accuracy deteriorates due to noise in eye-tracker data
Solution Approach 1:
The system performs preliminary actions by predicting future gaze positions based on historical gaze data and scene information before the actual gaze occurs. This allows the system to compensate for noise in real-time gaze data while maintaining quick response, as the prediction is prepared in advance using probabilistic models that incorporate scene context and object movement patterns.
Solution Approach 2:
The patent introduces an intermediary probabilistic prediction system between the raw gaze data and the object tracking output. This intermediary layer filters noise by combining multiple sources of information (historical gaze data, scene description, object positions) to generate a more accurate estimated gaze position, thereby improving tracking accuracy without sacrificing response speed.
2Measurement precision
If traditional filtering methods are applied to reduce noise in gaze data, then measurement precision improves, but the system complexity increases
Solution Approach 1:
The probabilistic prediction system serves multiple functions simultaneously: it filters noise from gaze data, predicts future gaze positions, incorporates scene context, and tracks moving objects. This multi-functionality allows the system to improve measurement precision without proportionally increasing complexity, as a single integrated model performs what would otherwise require multiple separate processing stages.
Solution Approach 2:
The system changes the parameters of the prediction model dynamically based on scene characteristics and object properties. By adjusting model parameters such as prediction horizon, noise variance, and scene context weightings, the system adapts to different conditions without requiring fundamentally different processing architectures, thereby managing complexity while maintaining precision.
3Reliability
If the system tracks only large objects, then tracking reliability is maintained, but the applicability to small objects deteriorates
Solution Approach 1:
The system performs preliminary prediction of gaze positions and object interactions before actual tracking occurs. This advance prediction allows the system to maintain reliability for small objects by anticipating their positions based on scene context and movement patterns, rather than relying solely on real-time gaze data which is too noisy for small target detection.
Solution Approach 2:
The probabilistic prediction system acts as an intermediary that bridges the gap between noisy gaze data and small object tracking. By generating predicted gaze positions that incorporate scene context and object priors, this intermediary layer enables reliable tracking of small objects that would otherwise be indistinguishable from noise in direct gaze tracking.
4Measurement precision
If probabilistic prediction is used to improve tracking accuracy, then measurement precision improves, but the computational requirements increase
Solution Approach 1:
The system applies partial probabilistic prediction by selectively modeling only the most relevant aspects of gaze behavior and scene context. Rather than performing exhaustive probabilistic calculations for all possible factors, the system focuses on key predictors such as recent gaze history and salient scene elements, achieving improved accuracy with reduced computational energy consumption.
Data Source
Figure 1a
Figure 1b
Figure 1c
AI summary
A system and method are provided for object tracking in a scene over time. The method comprises obtaining tracking data from a tracking device, the tracking data comprising information associated with at least one point of interest being tracked; obtaining position data from a scene information provider, the scene being associated with a plurality of targets, the position data corresponding to targets in the scene; applying a probabilistic graphical model to the tracking data and the target data to predict a target of interest associated with an entity being tracked; and performing at least one of: using the target of interest to determine a refined point of interest; and outputting at least one of the refined point of interest and the target of interest.