Event-Based Block-Matching Optical Flow Algorithm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional optical flow algorithms are not suitable for event-based vision sensors like DVS, as they are frame-based and struggle with dense textured inputs, and existing methods fail to provide efficient real-time computation on embedded platforms.
Innovation Solution
A new event-based block-matching algorithm that accumulates change events in three time slice frames and uses a distance metric to find the most similar search block, implemented both in software and on FPGA, allowing for efficient computation of optical flow with improved accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional frame-based optical flow algorithms are used with event-based vision sensors, then the algorithms can be directly applied to standard vision data, but they fail to work effectively with sparse event-based inputs and dense textured scenes
Solution Approach 1:
The patent transforms event-based sparse inputs into frame-based representations by accumulating events over time intervals, converting the data format to match conventional optical flow algorithm requirements while preserving the event-based sensor's advantages
Solution Approach 2:
The patent introduces an intermediate representation layer that converts event streams into pseudo-frame structures, allowing conventional algorithms to process event-based data without direct modification while maintaining accuracy on textured inputs
2Measurement precision
If block matching with dense image blocks is used to improve optical flow accuracy, then measurement precision is improved, but computational cost increases making real-time embedded implementation difficult
Solution Approach 1:
The patent divides the image into blocks and processes them independently through parallel hardware pipelines, enabling accurate block matching while maintaining real-time performance through concurrent computation
Solution Approach 2:
The patent replaces sequential software-based block matching with parallel hardware circuit implementation on FPGA, achieving both high accuracy and real-time performance by substituting software computation with dedicated hardware logic
3Productivity
If existing event-based optical flow methods are implemented on embedded platforms, then real-time processing is achieved, but they are not designed for logic circuit implementation and lack efficiency
Solution Approach 1:
The patent implements optical flow computation directly in hardware logic circuits on FPGA, replacing software algorithms with dedicated hardware pipelines that achieve real-time performance with optimized resource utilization
Solution Approach 2:
The patent designs a dynamic hardware architecture that adapts processing resources based on input event rates and computational requirements, optimizing performance for different scene complexities while maintaining real-time operation
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The invention relates to computation of optical flow using event-based vision sensors.