Event-Based Sensor Triggering for Low-Latency Hybrid Imaging
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Solution Overview
Problem
Existing imaging systems face challenges in efficiently processing high dynamic range and low-latency imaging tasks, particularly in scenarios involving fast motion and varying illumination, as traditional frame-based sensors require calibration and synchronization with event-based sensors, leading to increased computational load and power consumption.
Innovation Solution
Integration of frame-based and event-based sensors without the need for calibration or synchronization, where the event-based sensor generates asynchronous event streams to trigger frame-based sensor operations, optimizing data processing and reducing power consumption by selectively activating sensor subparts based on event data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional frame-based sensors are used for high dynamic range and low-latency imaging, then imaging quality is maintained, but computational load and power consumption increase due to calibration and synchronization requirements
Solution Approach 1:
The system segments the imaging function into two independent parts: event-based sensors that continuously monitor brightness changes asynchronously, and frame-based sensors that capture full frames only when triggered by event streams. This segmentation eliminates the need for continuous calibration and synchronization between sensors, reducing computational load and power consumption while maintaining imaging quality.
2Area of stationary object
If frame-based sensors operate at fixed frame rates, then complete scene coverage is achieved, but latency increases in fast motion scenarios
Solution Approach 1:
The system dynamically adapts the frame capture rate based on scene activity. Event-based sensors continuously monitor brightness changes and trigger frame-based sensors only when significant changes occur. This dynamic approach maintains complete scene coverage while reducing latency in fast motion scenarios by capturing frames asynchronously rather than at fixed intervals.
3Measurement precision
If multiple sensors are integrated for enhanced functionality, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The event stream acts as an intermediary between event-based sensors and frame-based sensors. Event-based sensors generate asynchronous event streams that trigger frame capture only when needed, eliminating the need for complex continuous synchronization mechanisms. This intermediary approach maintains detection accuracy while reducing system complexity.
4Use of energy by moving object
If event-based sensors trigger frame-based sensors asynchronously, then power consumption is reduced, but synchronization challenges arise
Solution Approach 1:
The event stream processor autonomously analyzes event streams and autonomously generates trigger signals for frame-based sensors without requiring external synchronization control. This self-service approach allows asynchronous operation that reduces power consumption while avoiding complex synchronization mechanisms.
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
Enhances imaging system performance by reducing computational load and power consumption while improving detection accuracy and efficiency in dynamic environments, enabling robust installation and self-diagnostic capabilities.
Implementation Method 1
event-based sensors that, differently than traditional frame-based sensors, do not capture image brightness at a fixed rate, but rather asynchronously measure brightness changes on a per-pixel basis
Implementation Method 2
a plurality of laser projectors associated with each of the event-based sensors, and configured to project a complex laser pattern in a corresponding field-of-view of the associated event-based sensor
Data Source
AI summary
The disclosure includes imaging systems employing event-based sensors in various applications for analyzing a scene, such as those involved in conveyor systems, mass flow detection systems, portals, machine vision systems, retail settings including checkout stations, and the like. Event-based sensors may be operated in conjunction with frame-based cameras to select frames or subparts of frames or otherwise trigger actions related to the imaging system based on analysis of the events generated by the event-based sensors.