Event Signal Processing Using 3D Voxel Grid Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current signal processing technologies for event detection, such as those using color image sensors, require high computational power and hardware resources to differentiate between moving objects and background changes, leading to increased energy consumption and costs.
Innovation Solution
A method and apparatus that utilize a 3D grid to map event coordinates and timestamps from an event-based sensor, updating voxel values based on event counts and correlations, allowing for efficient activity detection and mode switching between low energy and full resolution modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frame image based sensor is used to detect activity by analyzing consecutive images, then activity detection capability is improved, but computational load and energy consumption increase
Solution Approach 1:
The patent segments the continuous image stream into discrete event triggers, where only pixels experiencing significant changes (above a threshold) generate events. This segmentation transforms the continuous processing burden into discrete, sparse event streams that require minimal computational resources while maintaining activity detection capability.
Solution Approach 2:
The event-based sensor operates in a periodic sampling manner, continuously monitoring pixel states and generating events only when changes exceed a threshold. This periodic event generation replaces continuous frame processing, reducing computational load while maintaining detection reliability through timely event triggers.
2Reliability
If frame image based sensor is used to detect activity by analyzing consecutive images, then activity detection capability is improved, but hardware resources and processing complexity increase
Solution Approach 1:
The patent extracts only the essential information needed for activity detection by having the event-based sensor output only changed pixel coordinates and timestamps, discarding redundant unchanged pixel data. This extraction simplifies the input data structure from complete frames to minimal event representations, reducing processing complexity.
Solution Approach 2:
Instead of processing complete frames and identifying changes, the system inverts the approach by having sensors directly output only the changed elements (events). This inversion shifts the processing burden from analyzing entire frames to interpreting sparse event streams, significantly simplifying the processing architecture.
3Use of energy by moving object
If event based sensor is used to reduce computational load, then energy consumption is reduced, but noise filtering capability must be improved
Solution Approach 1:
The system applies feedback mechanisms by continuously updating voxel values based on accumulated event counts and comparing them against thresholds. This feedback loop enables dynamic noise filtering, where only events consistent with sustained patterns (exceeding thresholds) trigger activity detection, effectively filtering random noise while maintaining low energy consumption.
Solution Approach 2:
The patent performs preliminary accumulation of events in 3D voxels before making detection decisions. By pre-accumulating event counts and spatial-temporal patterns in the voxel grid, the system establishes a foundation for noise filtering that distinguishes genuine activities from random noise events, reducing false positives without increasing energy consumption.
Data Source
AI summary
Provided is an event signal processing method and apparatus. The event signal processing method includes acquiring, from an event based sensor, coordinates of an active pixel corresponding to an event and a timestamp corresponding to the event; mapping the event to a target voxel included in a three-dimensional (3D) grid based on the coordinates of the active pixel and the timestamp; and updating a value of the target voxel based on the event.