Overlapping Time Windows for Event-Based Vision Sensor Frame Accumulation
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Solution Overview
Problem
Conventional computer vision methods struggle to effectively process the event stream from event-based vision sensors due to the absence of frames, particularly for applications involving continuously moving objects.
Innovation Solution
The method involves using overlapping time windows to accumulate events into image frames, where the first and second image frames are elaborated using events from overlapping time windows, improving the precision of subsequent computer vision algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision methods are used to process event stream data, then processing speed is maintained, but precision is degraded due to the absence of frames and large differences in events between non-overlapping time windows
Solution Approach 1:
The event stream is segmented into multiple overlapping time windows instead of non-overlapping windows. Each time window captures events with temporal overlap, ensuring that transitions and movements are captured in multiple consecutive frames. This segmentation strategy reduces information loss at window boundaries and improves precision for detecting fast-moving objects.
Solution Approach 2:
The method performs preliminary accumulation of events in overlapping time windows before applying computer vision algorithms. By pre-processing the event stream with overlapping windows, the system prepares enhanced frame data that preserves temporal continuity, thereby improving subsequent algorithm precision without requiring changes to the algorithms themselves.
2Productivity
If non-overlapping time windows are used to accumulate events, then device complexity is reduced, but productivity is degraded due to rigid and discontinuous frame integration
Solution Approach 1:
The system dynamically manages overlapping time windows by continuously accumulating events in sliding temporal intervals. This dynamic approach allows frames to be generated continuously with temporal overlap, improving productivity for continuous motion detection. The complexity is managed through systematic event buffering and timestamp-based filtering rather than complex hardware modifications.
Solution Approach 2:
The overlapping time windows enable continuous accumulation of events without gaps between frames. This continuity ensures that no motion events are missed at window boundaries, maintaining productive continuous monitoring. The method keeps the data accumulation process ongoing by overlapping windows, thereby eliminating idle periods between frame generations.
3Adaptability or versatility
If fixed integration time is used for event accumulation, then device complexity is minimized, but adaptability is reduced for varying motion speeds and conditions
Solution Approach 1:
The system changes the temporal parameter of time windows by using overlapping intervals that can be adjusted based on motion characteristics. This parameter change allows the system to adapt to varying motion speeds - faster motion can be captured with shorter or more overlapping windows, while slower motion uses longer intervals. The adaptability is achieved through flexible time window configuration rather than hardware changes.
Data Source
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AI summary
A method for elaborating image frames using an event-based vision sensor (PM) which comprises a plurality of pixels and is configured to output a stream of change events (ES), wherein each change event corresponds to a change in brightness detected by one pixel and comprises a time stamp, a pixel address, and a parameter describing a sign of said brightness change, wherein a first image frame and a second image frame are elaborated, using change events accumulated in buffers, these change events appearing in time windows which have a relative time position. The invention also concerns the corresponding system.