Advisor

Ball, John E.

Committee Member

Archibald, Christopher

Committee Member

Gurbuz, Ali

Committee Member

Fowler, James E.

Date of Degree

8-1-2019

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Many image processing algorithms exist that can accurately detect humans and other objects such as vehicles and animals. Many of these algorithms require large amounts of processing often requiring hardware acceleration with powerful central processing units (CPUs), graphics processing units (GPUs), field programmable gate arrays (FPGAs), etc. Implementing an algorithm that can detect objects such as humans at longer ranges makes these hardware requirements even more strenuous as the numbers of pixels necessary to detect objects at both close ranges and long ranges is greatly increased. Comparing the performance of different low-power implementations can be used to determine a trade-off between performance and power. An image differencing algorithm is proposed along with selected low-power hardware that is capable of detected humans at ranges of 500 m. Multiple versions of the detection algorithm are implemented on the selected hardware and compared for run-time performance on a low-power system.

URI

https://hdl.handle.net/11668/14484

Comments

object detection||image detection||low-power||long-range||high-resolution||image differencing||frame differencing||morphology||multi-threading||hardware acceleration||ARM||Vivante||NXP||GPU||CPU

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