Gesture Recognition System for Vehicle Input
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Solution Overview
Problem
Current driver input systems for vehicles, such as touch screens and remote control graphical displays, are distracting and dangerous as they require operators to divert their eyes from the road to enter information, and gesture recognition systems face challenges in adverse conditions like illumination changes and occlusions.
Innovation Solution
A gesture recognition system that fuses multiple information sources to improve robustness and accuracy, using a gesture capture module with adjustable parameters, Bayesian model adaptation, and feature fusion to enhance hand localization and skin color detection, allowing drivers to input information via spatial hand gestures on a surface within the vehicle without looking away.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If touch screens or remote control graphical displays are used for driver input, then information can be entered into the vehicle system, but the driver must divert eyes from the road causing distraction and danger
Solution Approach 1:
The patent replaces traditional mechanical input devices (touch screens, remote controls) with a gesture recognition system using optical sensors and image processing. The system captures images of hand gestures, processes them through algorithms to recognize spatial patterns, and translates them into commands, eliminating the need for drivers to physically interact with controls that require visual attention.
2Reliability
If gesture recognition is used for driver input, then driving safety can be improved by keeping eyes on the road, but illumination changes, reflections, shadows, and occlusions make robust detection challenging
Solution Approach 1:
The patent merges multiple information sources and processing approaches to overcome environmental challenges. It combines data from multiple sensors (cameras, depth sensors), integrates multiple gesture recognition algorithms, and fuses results from different processing methods (heuristic approaches, statistical models, machine learning) to achieve robust gesture detection that compensates for illumination changes, reflections, shadows, and occlusions.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on environmental conditions. It modifies sensitivity thresholds, adjusts image processing filters, and adapts recognition criteria according to lighting conditions, camera angles, and detected gesture characteristics, enabling consistent performance across varying operational environments.
3Reliability
If multiple information sources are fused in gesture recognition, then reliability and robustness improve, but system complexity increases
Solution Approach 1:
The patent segments the gesture recognition system into distinct functional modules: data acquisition from multiple sensors, pre-processing of sensor data, feature extraction, gesture classification, and command generation. Each module handles specific tasks independently, allowing the complex multi-source fusion to be managed through modular architecture that simplifies implementation and maintenance while maintaining high reliability.
Data Source
AI summary
A method of interpreting input from a user includes providing a surface within reach of a hand of the user. A plurality of locations on the surface that are touched by the user are sensed. An alphanumeric character having a shape most similar to the plurality of touched locations on the surface is determined. The determining includes collecting information associated with hand region localized modules, and modeling the information using statistical models. The user is informed of the alphanumeric character and/or a word in which the alphanumeric character is included. Feedback is received from the user regarding whether the alphanumeric character and/or word is an alphanumeric character and/or word that the user intended to be determined in the determining step.