Date Thesis Awarded
5-2022
Access Type
Honors Thesis -- Access Restricted On-Campus Only
Degree Name
Bachelors of Science (BS)
Department
Computer Science
Advisor
Bin Ren
Committee Members
Qun Li
Chi-Kwong Li
Abstract
Recognizing hand gesture detection is becoming increasingly essential as humans and computers interact closer and closer; we propose a framework for near real-time 3-D hand gesture reconstruction on mobile devices. Specifically, we accomplished a whole-application optimization on the hand gesture recognition and rendering task by parallelism and pipelining. With optimization of the model itself, we can achieve a near-real-time performance on Android cell phones, with the core design idea applicable to other platforms. Moreover, we present extensive data on partition tasks to achieve efficient pipelining.
Recommended Citation
Wang, Charles, "Accelerating Gesture Detection DNNs on Mobile Devices with Model Pruning and Whole Application Optimization Co-Design" (2022). Undergraduate Honors Theses. William & Mary. Paper 1818.
https://scholarworks.wm.edu/honorstheses/1818
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This work is licensed under a Creative Commons Attribution 4.0 License.
