Vehicle Vision System Gesture Recognition with Habitual Movement Learning
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Solution Overview
Problem
Current vehicle gesture recognition systems face challenges in accurately distinguishing between habitual movements and intended gestures, leading to high false detection rates and driver frustration, especially when capturing hand movements in a 3D space using cameras.
Innovation Solution
A vehicle vision system that utilizes one or more CMOS cameras to capture image data and includes a learning algorithm that identifies habitual movements of the driver by processing repetitive actions over time, using a Bayesian classifier to differentiate between habitual actions and intended gestures, thereby reducing false positive recognition rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If gesture recognition systems use cameras to capture hand movements in 3D space, then the control interface becomes more intuitive, but false detection rates increase due to inability to distinguish habitual movements from intended gestures
Solution Approach 1:
The system performs preliminary learning of the driver's habitual movements during a calibration phase before actual gesture control is activated. This preliminary action establishes a baseline of normal movements that the system can later distinguish from intentional gestures, thereby reducing false detections while maintaining intuitive control.
Solution Approach 2:
The system incorporates feedback mechanisms where the learned habitual movements continuously inform the gesture recognition algorithm. By comparing current hand movements against the stored habitual patterns, the system provides feedback that helps distinguish between unintentional habitual movements and intentional gestures, reducing false positives while preserving ease of use.
2Reliability
If the system learns and recognizes habitual movements to reduce false detections, then reliability improves, but device complexity increases due to learning algorithms
Solution Approach 1:
The system performs self-learning of habitual movements automatically during normal operation without requiring external calibration tools or complex user setup procedures. The learning algorithm adapts to the driver's behavior patterns autonomously, improving reliability while keeping the user interface simple and the overall system complexity manageable.
Solution Approach 2:
The system changes parameters such as movement velocity thresholds, acceleration patterns, and temporal characteristics to differentiate between habitual movements and intentional gestures. By dynamically adjusting these parameters based on learned behavior, the system improves recognition accuracy without requiring overly complex algorithmic structures.
Data Source
AI summary
A vehicular vision system includes an in-cabin camera having a field of view that encompasses at least a hand of a driver of the vehicle. A control includes an image processor operable to process image data captured by the in-cabin camera. The control, responsive to image processing by the image processor of image data captured by the in-cabin camera, is operable to determine a movement of a hand of the vehicle driver. Responsive to determination of the movement of the driver's hand, the control is operable to determine if the movement is indicative of a gesture for controlling an accessory of the vehicle. Responsive to the determined gesture, the control may control pitch of another camera, yaw of another camera, roll of another camera, and/or a virtual viewing angle of a virtual camera of the bird's eye view system.