Stumpp, Daniel Charles
(2022)
Optimization and Hardware Acceleration of Event-Based Optical Flow for Real-Time Processing and Compression on Embedded Platforms.
Master's Thesis, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Event-based vision sensors produce asynchronous event streams with high temporal resolution based on changes in the visual scene. The properties of these sensors allow for accurate and fast calculation of optical flow as events are generated. Existing solutions for calculating optical flow from event data either fail to capture the true direction of motion due to the aperture problem, do not use the high temporal resolution of the sensor, or are too computationally expensive to be run in real time on low-power embedded platforms. In this research, software optimization of the existing ARMS (Aperture Robust Multi-Scale flow) algorithm is performed. The new optimized software version (fARMS) significantly improves throughput on a traditional CPU. Further, we present hARMS, a hardware realization of the fARMS algorithm allowing for real-time computation of true flow on low-power, embedded platforms. The proposed hARMS architecture targets hybrid system-on-chip (SoC) devices and was designed to maximize configurability and throughput. The hardware architecture and fARMS algorithm were developed with asynchronous neuromorphic processing in mind, abandoning the common use of an event frame and instead operating using only a small history of relevant events, allowing latency to scale independently of the sensor resolution. This change in processing paradigm improved the estimation of flow directions by up to 73\% compared to the original ARMS algorithm and yielded a demonstrated hARMS throughput of up to 1.21 Mevent/s on the benchmark configuration selected. This throughput enables real-time performance and makes it the fastest known realization of aperture-robust, event-based optical flow to date. Finally, the fast event-based optical flow enabled by the fARMS and hARMS designs is leveraged for use in a novel flow-based event-stream compression algorithm. This compression method enables communication across low-bandwidth mediums, while allowing for accurate reconstruction of the event stream.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
10 June 2022 |
| Date Type: |
Publication |
| Defense Date: |
7 April 2022 |
| Approval Date: |
10 June 2022 |
| Submission Date: |
15 March 2022 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
74 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Event-based, aperture robust, optical flow, neuromorphic computing, field programmable gate arrays, system-on-chip, parallel acceleration, real-time systems, compression |
| Date Deposited: |
10 Jun 2022 19:06 |
| Last Modified: |
10 Jun 2023 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/42396 |
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Optimization and Hardware Acceleration of Event-Based Optical Flow for Real-Time Processing and Compression on Embedded Platforms. (deposited 10 Jun 2022 19:06)
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