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Deep Image Processing with Spatial Adaptation and Boosted Efficiency & Supervision for Accurate Human Keypoint Detection and Movement Dynamics Tracking

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis aims to design and develop the spatial adaptation approach through spatial transformers to improve the accuracy of human keypoint recognition models. We have studied different model types and design choices to gain an accuracy increase over models without spatial transformers and analyzed how spatial transformers increase the accuracy of predictions. A neural network called Widenet has been leveraged as a specialized network for providing the parameters for the spatial transformer. Further, we have evaluated methods to reduce the model parameters, as well as the strategy to enhance the learning supervision for further improving the performance of the model. Our experiments and results have shown that the proposed deep learning framework can effectively detect the human key points, compared with the baseline methods. Also, we have reduced the model size without significantly impacting the performance, and the enhanced supervision has improved the performance. This study is expected to greatly advance the deep learning of human key points and movement dynamics.

Identiferoai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/33375
Date05 1900
CreatorsDai, Chao Yang
ContributorsZhang, Qingxue, King, Brian S., Fang, Shiaofen
Source SetsIndiana University-Purdue University Indianapolis
LanguageEnglish
Detected LanguageEnglish
TypeThesis
RightsAttribution 4.0 International, http://creativecommons.org/licenses/by/4.0/

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