Event-Based Vision Sensor Threshold Control for Stable Data Rates
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Solution Overview
Problem
Conventional frame-based image sensors produce excessive redundant data, leading to high power consumption, limited dynamic range, poor low-light performance, and motion blur, which are inefficient for tasks like tracking and position estimation due to the correspondence problem and computational intensity.
Innovation Solution
A change detection sensor with an event rate detector and controller that adjusts pixel configuration to modulate the event rate, using counters or estimators to manage the number of events, ensuring efficient data processing and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional frame-based image sensors are used to capture visual data, then complete image frames are obtained, but excessive redundant data is produced leading to high power consumption and limited temporal resolution
Solution Approach 1:
The event-based sensor extracts only the essential information from the visual scene by detecting and encoding only the position and magnitude of changes in luminance, rather than capturing complete image frames. This extraction principle removes redundant data while preserving critical visual information, directly reducing data volume and power consumption.
Solution Approach 2:
The sensor dynamically adapts its operation by continuously monitoring luminance changes and generating events only when changes exceed a threshold. This dynamic behavior allows the system to adjust its data output based on scene activity, consuming power only when necessary to detect changes, thereby resolving the contradiction between information completeness and power efficiency.
2Measurement precision
If conventional frame-based cameras capture sequential images for motion estimation, then position and orientation can be estimated, but the correspondence problem requires significant processing power and time
Solution Approach 1:
The event-based sensor extracts only the essential information from the visual scene by detecting and encoding only the position and magnitude of changes in luminance, rather than capturing complete image frames. This extraction principle removes redundant data while preserving critical visual information, directly reducing data volume and power consumption.
Solution Approach 2:
The sensor dynamically adapts its operation by continuously monitoring luminance changes and generating events only when changes exceed a threshold. This dynamic behavior allows the system to adjust its data output based on scene activity, consuming power only when necessary to detect changes, thereby resolving the contradiction between information completeness and power efficiency.
3Speed
If the event rate from the change detection sensor is increased to capture more scene changes, then temporal resolution is improved, but data processing load and power consumption increase
Solution Approach 1:
The sensor employs adaptive thresholding where the luminance change threshold is dynamically adjusted based on scene conditions. When scene activity is high, thresholds are increased to reduce event rate and processing load. When scene activity is low, thresholds are decreased to maintain temporal resolution. This parameter adaptation resolves the contradiction between speed and processing efficiency.
Solution Approach 2:
The system implements feedback control by monitoring the event rate and adjusting the detection threshold accordingly. When the event rate exceeds a certain level, the threshold is increased to reduce the data processing load. This feedback mechanism ensures that temporal resolution is maintained while preventing excessive data generation, thereby resolving the contradiction between speed and productivity.
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
The system effectively controls the event rate to optimize data processing, reduce power consumption, and enhance dynamic range and low-light performance, addressing the inefficiencies of conventional sensors.
Implementation Method 1
A change detection sensor provides high temporal resolution, low latency, low power consumption, high dynamic range with little motion blur
Data Source
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AI summary
In dynamic vision sensor (DVS) or change detection sensors, the chip or sensor is configured to control or modulate the event rate. For example, this control can be used to keep the event rate close to a desired rate or within desired bounds. Adapting the configuration of the sensor to the scene by changing the ON-event and/or the OFF-event thresholds, allows having necessary amount of data, but not much more than necessary, such that the overall system gets as much information about its state as possible.