Event-Based Sensor Imaging for Low-Latency Frame Capture
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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 inefficiencies in power consumption and computational load.
Innovation Solution
Integration of frame-based and event-based sensors without the need for calibration or synchronization, where the event-based sensor generates trigger signals based on event streams to selectively process image frames, reducing unnecessary computations and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional frame-based sensors are used for imaging, then image quality can be maintained, but power consumption increases and latency is high due to fixed-rate capture
Solution Approach 1:
The system transitions from fixed-rate frame capture to dynamic event-driven capture. The event-based sensor continuously monitors brightness changes and only triggers frame capture when events exceed a threshold, making the capture rate adaptive to scene activity rather than static.
Solution Approach 2:
The event-based sensor autonomously determines when frame capture is necessary by analyzing brightness change events, eliminating the need for external synchronization signals or calibration procedures. The system self-regulates based on scene content.
2Measurement precision
If frame-based sensors capture images at fixed high rates, then motion capture quality improves, but unnecessary computations increase power consumption
Solution Approach 1:
The system extracts only the essential information needed for motion detection from continuous scene monitoring. By filtering through event-based detection and thresholding, it extracts only those moments when actual motion occurs, discarding redundant frame data.
Solution Approach 2:
The system dynamically changes the capture parameter (frame rate) based on scene activity level. When brightness change events are low, capture rate decreases; when events exceed threshold, capture rate increases, optimizing the balance between motion capture quality and energy consumption.
3Measurement precision
If calibration and synchronization between frame-based and event-based sensors are implemented, then system accuracy improves, but device complexity and installation difficulty increase
Solution Approach 1:
The event-based sensor acts as an intermediary that naturally bridges the two sensing modalities. Its event output serves as the triggering mechanism for frame capture, eliminating the need for separate calibration procedures between sensors.
Solution Approach 2:
The system merges the functions of motion detection and frame capture into a unified event-driven workflow. The event-based sensor and frame-based sensor are combined such that one directly controls the other without requiring independent calibration.
4Loss of time
If event-based sensors trigger frame capture based on brightness changes, then latency is reduced, but system complexity increases
Solution Approach 1:
The event-based sensor performs preliminary monitoring and analysis of brightness changes continuously in the background, preparing trigger signals before actual frame capture is needed. This preliminary action enables immediate response when motion occurs.
Solution Approach 2:
The system replaces traditional mechanical or electronic synchronization mechanisms with an event-based software trigger system. The stream processor analyzes event streams and generates trigger signals programmatically, substituting complex hardware synchronization with flexible software control.
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 improving latency, dynamic range, and reducing power consumption while enabling advanced functionalities such as self-diagnosis and precise parcel detection, even for thin parcels, with simplified installation and reduced infrastructure costs.
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
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.