Event-Based Sensor Motion Analysis Using Velocity Direction Tables
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Solution Overview
Problem
Conventional methods for analyzing motion using event-based sensors are inadequate in outdoor environments with significant luminance changes, as they fail to recognize motion sufficiently.
Innovation Solution
A method and system that utilize an event-based sensor to receive and process luminance change events, calculating velocities and directions, and generating a motion analysis table to determine subject motion by classifying events into velocity and direction sections, with criteria for determining motion direction and velocity based on frequency and baseline ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to analyze motion using event-based sensors, then the system is simple to operate, but motion recognition is insufficient in outdoor environments with huge luminance changes
Solution Approach 1:
The patent segments the analysis process into distinct stages: event generation from luminance changes, velocity calculation, direction determination, and motion analysis table generation. Each stage processes specific parameters independently, improving motion recognition accuracy without requiring complete system redesign.
Solution Approach 2:
The patent introduces a new dimension of analysis by generating a motion analysis table that combines velocity and direction information. This tabular representation adds a structural layer to the data, enabling more accurate motion detection in varying luminance conditions without increasing hardware complexity.
2Reliability
If the system processes all luminance change events, then comprehensive motion data is captured, but false motion detection increases in high luminance change environments
Solution Approach 1:
The patent applies local quality by analyzing events within specific velocity and direction sections rather than treating all events uniformly. By categorizing events into discrete sections and identifying representative directions for each section, the system improves reliability while filtering out false positives from high luminance change environments.
Solution Approach 2:
The patent changes parameters by calculating velocity and direction from raw event data, then using these transformed parameters to populate the motion analysis table. This parameter transformation allows the system to distinguish true motion from luminance artifacts by analyzing the distribution patterns across velocity and direction sections.
3Measurement precision
If velocity sections are evenly distributed, then the motion analysis table is simple to generate, but accuracy decreases when frame average velocity falls outside normal motion ranges
Solution Approach 1:
The patent implements dynamics by adjusting the intervals of velocity sections based on the frame average velocity. When the frame average velocity falls outside normal ranges, the system dynamically modifies the velocity section intervals to ensure accurate measurement, rather than using fixed evenly-distributed sections.
Solution Approach 2:
The system uses feedback by comparing the frame average velocity against normal motion ranges and adjusting the velocity section intervals accordingly. This feedback mechanism ensures that the motion analysis table maintains measurement precision across varying luminance conditions and velocity ranges.
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 recognizes subject motion and distinguishes it from background with reduced error, accurately determining motion direction and velocity in varying luminance conditions.
Implementation Method 1
An event-based sensor (EBS) is a type of image sensor that generates events corresponding to a luminance change of a subject
Data Source
AI summary
A method is provided for analyzing motion of a subject. The method includes receiving multiple events from an event-based sensor, the events corresponding to a luminance change of the subject; calculating velocities of the events and directions of the events; generating a motion analysis table comprising an X axis classified into multiple velocity sections, a Y axis classified into multiple direction sections, and multiple cells representing frequencies of events corresponding to combinations of the velocity sections and the direction sections; and determining a motion of the subject based on the motion analysis table.