Who\u27s That Pokemon: Pokedex Project

Abstract The goal of the Pokèdex is to help those who are new to Pokèmon identify the creatures and to bring to life the tool from the show for nostalgic fans. Objectives In order to accomplish this, we utilized an Object Detection API with Tensorflow using Python. Our application will highlight the detected Pokemon by surrounding it with a box and a label with the Pokemon\u27s name and the program\u27s percent confidence. People can expect a GUI that takes a picture of a Pokémon with a scan button. The program will scan the Pokemon in frame and use our Object Detection API to distinguish said Pokémon. Artist Statement Our motivation to develop this application was driven by our love for the Pokèmon show and desire to apply skills learned throughout our time at Pacific as well as learning topics in a field we are all interested in - machine learning (ML) and artificial intelligence (AI). We hope that audiences are able to see the fluidity of the program and quick results. The quick and certain results are as a result of hours of hard work poured into this project. We hope that others will share the same joy for the project as we do and really just have an enjoyable experience while using our application. It was enjoyable to research this project, since it did involve a lot of looking up Pokemon pictures and stats - which isn’t the typical homework assignment

Who\u27s That Pokemon: Pokedex Project

Abstract The goal of the Pokèdex is to help those who are new to Pokèmon identify the creatures and to bring to life the tool from the show for nostalgic fans. Objectives In order to accomplish this, we utilized an Object Detection API with Tensorflow using Python. Our application will highlight the detected Pokemon by surrounding it with a box and a label with the Pokemon\u27s name and the program\u27s percent confidence. People can expect a GUI that takes a picture of a Pokémon with a scan button. The program will scan the Pokemon in frame and use our Object Detection API to distinguish said Pokémon. Artist Statement Our motivation to develop this application was driven by our love for the Pokèmon show and desire to apply skills learned throughout our time at Pacific as well as learning topics in a field we are all interested in - machine learning (ML) and artificial intelligence (AI). We hope that audiences are able to see the fluidity of the program and quick results. The quick and certain results are as a result of hours of hard work poured into this project. We hope that others will share the same joy for the project as we do and really just have an enjoyable experience while using our application. It was enjoyable to research this project, since it did involve a lot of looking up Pokemon pictures and stats - which isn’t the typical homework assignment

Performance Evaluation of Markerless 3D Skeleton Pose Estimates with Pop Dance Motion Sequence

The evaluation of markerless pose estimation performed by OpenPose has been getting much attention from researchers of human movement studies. This work aims to evaluate and compare the output joint positions estimated by the OpenPose with a marker-based motion-capture data recorded on a pop dance motion. Although the marker-based motion capture can accurately measure and record the human joint positions, this particular set-up is expensive. The framework to compare the outputs of the markerless method to the ground truth marker-based joint remains unknown, especially for complex body motion. Synchronization, camera calibration, and 3D reconstruction by fusing the outputs of the markerless method (OpenPose) are discussed. In this case study, the comparison results illustrate that the mean absolute errors for each key points are less than 700 mm. Contribution: This work contributes for human movement science by evaluating the OpenPose markerless 3D reconstruction pose with the marker-based motion-capture data recorded on pop dance motion