Image Feature Sampling Architecture for Low-Latency Motion Tracking
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Solution Overview
Problem
Conventional processing systems experience mismatches in processing and data transfer speeds, leading to reduced system performance and inability to handle complex computational processes such as image processing or graphics processing, particularly in environments requiring rapid and accurate motion detection for autonomous navigation.
Innovation Solution
An image data sampling architecture utilizing a first memory device, a second memory device, processors including a decoupled lookup table, pixel processing engines, and vision processing units, with hardware sequencers to efficiently extract and process template image features, reducing computational complexity and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processing systems are used, then system simplicity is maintained, but processing speed and data transfer speed mismatch reduces overall system performance
Solution Approach 1:
The processing system is divided into distinct specialized components: a first processing unit for initial image processing, a second processing unit for motion detection, and a hardware sequencer for data transfer coordination. Each unit handles specific tasks independently, eliminating speed mismatches while maintaining overall system efficiency through modular architecture.
2Productivity
If conventional processing systems are used, then hardware complexity is reduced, but the ability to handle complex computational processes such as image processing or graphics processing is reduced or eliminated
Solution Approach 1:
A hardware sequencer acts as an intermediary component between the first and second processing units, coordinating data transfer and synchronization. This mediator enables complex computational processes by managing the interaction between specialized processing units without requiring the entire system to operate at the same speed, thus enabling advanced image processing while controlling overall hardware complexity.
3Measurement precision
If CPU processing or general GPU processing is used, then system simplicity is maintained, but high-speed and accurate motion detection beyond conventional processing capability cannot be achieved
Solution Approach 1:
The system applies local quality by giving each processing unit specialized functionality optimized for its specific task. The first processing unit is optimized for general image processing while the second processing unit is specifically optimized for motion detection algorithms. This specialization enables high-speed and accurate motion detection by dedicating hardware resources to specific computational requirements rather than using general-purpose processors.
Data Source
AI summary
Aspects of this technical solution can increase speed of processing and lower computational hardware complexity in motion detection, while maintaining integrity of motion detection across image frames. For example, in image-processing environments associated with autonomous or semi-autonomous navigation (e.g., driving, robotic navigation, etc.), a large volume of image data is to be rapidly and accurately processed to maintain reliable and up-to-date models of a physical environment. Thus, embodiments in accordance with this disclosure can provide high-speed and accurate motion detection of input frame data.