Adaptive Distance Estimation for Imaging Systems
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Solution Overview
Problem
Imaging systems face challenges in maintaining high efficiency while ensuring accuracy and clarity, particularly in applications like vehicle object detection where power demand is high and accuracy is critical.
Innovation Solution
The implementation of adaptive distance estimation techniques using time-of-flight principles, where phase delay values from reflected light are calculated based on multiple frame events, allowing for dynamic reference phase selection and joint processing of phase information to improve distance measurement accuracy and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple frame events are processed to improve distance measurement accuracy, then measurement precision improves, but use of energy increases
Solution Approach 1:
The system dynamically adjusts the number of frame events processed based on detected object motion. When motion is detected, fewer frame events are processed to reduce power consumption while maintaining adequate accuracy. When no motion is detected, more frame events are processed to improve distance measurement accuracy, thus adaptively resolving the contradiction between measurement precision and power consumption.
Solution Approach 2:
The system changes the parameter of frame event processing quantity based on motion detection results. By detecting object motion and adjusting the number of frame events accordingly, the system optimizes the balance between measurement accuracy and power consumption, processing more frames when stationary and fewer frames when motion is present.
2Use of energy by moving object
If adaptive reference phase selection is implemented to reduce power consumption, then use of energy decreases, but measurement precision may be compromised
Solution Approach 1:
The system changes the reference phase parameter adaptively based on motion detection. When no motion is detected, more reference phases are used to improve measurement precision. When motion is detected, fewer reference phases are processed to reduce power consumption, thus adaptively balancing power consumption and measurement precision through parameter adjustment.
Solution Approach 2:
The reference phase selection process becomes dynamic rather than static. The system dynamically determines which reference phases to process based on real-time motion detection, allowing the measurement system to adapt its precision level to current operational conditions, thereby reducing unnecessary power consumption while maintaining adequate accuracy.
3Productivity
If high frame rate processing is used to improve object detection speed, then productivity increases, but use of energy increases
Solution Approach 1:
The system uses periodic motion detection checks to determine processing intensity. Instead of continuously processing all frames at high rate, the system periodically detects motion and adjusts processing accordingly, maintaining high detection capability when needed while reducing power consumption during static periods through selective frame processing.
Solution Approach 2:
The frame processing rate is dynamically adjusted based on motion detection results. When motion is detected, the system processes frames at higher rates to maintain detection productivity. When no motion is detected, the processing rate is reduced to lower power consumption, thus dynamically balancing productivity and energy usage.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and efficiency of imaging systems by adaptively selecting reference phases and processing multiple frame events, thereby improving distance estimation and reducing power usage, especially in applications like vehicle object detection.
Implementation Method 1
Time-of-flight cameras, for example, may use imaging devices to measure the distance of an object from the camera
Implementation Method 2
distance calculations may be based on phase delays in the reflected light radiation
Data Source
AI summary
Representative implementations of devices and techniques provide adaptive distance estimation for imaging devices and systems. Distance estimation may be based on phase delays in reflected light. Reference phases used to determine phase delay values may be changed for subsequent frame events. Multiple frame events may be selected for some distance calculations based on whether object movement is detected within a predetermined area.