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Data & Code
This page contains links to training and test data, source code and evaluation programs, provided by members of our lab. The goal is to promote the scientific use of our work and help advance research in computer vision and pattern recognition. Please feel free to contact individual authors or members of our labs if you have questions or suggestions.
Description: The PSU Taiji MultiModal (PSU-TMM100) Dataset contains 100 Taiji motion sequences, 24-form Simplified Taiji-Quan, aka Tai Chi, performed by 10 human subjects. The dataset includes time-synchronized measurements: - motion capture markers and body joints recorded using Vicon Nexus 2.6.1 - foot pressure recorded using Tekscan F-scan 7.0 insole sensors - 1080p video from two views recorded using Vicon Nexus 2.6.1 Additionally, OpenPose and HRNet pose detection networks networks are applied on both video views to generate 2D and triangulated 3D vision-based joints estimates.If you use this dataset for publication, we kindly ask you to attribute credit by citing the ECCV2020 Paper providing detailed description of the dataset and benchmarks:
@inproceedings{Scott2020,
title = {From Image to Stability: Learning Dynamics from Human Pose},
author = {Scott, Jesse and Ravichandran, Bharadwaj and Funk, Christopher and Collins, Robert T. and Liu, Yanxi},
booktitle = {Computer Vision -- ECCV 2020},
pages = {536--554},
isbn = {978-3-030-58592-1},
year = {2020},
editor = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
publisher = {Springer International Publishing},
address = {Cham},
doi = {10.1007/978-3-030-58592-1_32},
}
Access Requests: Dataset Request Form Contact: Keaton Kraiger, Yanxi Liu This dataset collection is carried out under PSU IRB STUDY8085, by members of PSU LPAC, primarily by (now Dr.) Jesse Scott advised by professors Robert Collins (CSE), John Challis (Kinesiology), and Yanxi Liu (EE and CSE). This work is supported in part by NSF grant IIS-1218729 and the College of Engineering Dean's office of Penn State University.
Description: This database contains textures from completely regular to completely irregular patterns, with a focus on near-regular textures. It also includes video of near-regular textures in motion. Also available: groundtruth for translation, rotation and reflection/glide-reflection symmetry detection algorithms. Link: https://vivid.cse.psu.edu/ Contact: Shimian Zhang, Yanxi Liu
Description: Ground truth trajectory and grouping information for pedestrians walking in the PSU student union building. This is dataset "SU2" described in the paper: Weina Ge, Robert Collins, and Barry Ruback, "Vision-based Analysis of Small Groups in Pedestrian Crowds" IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear, May 2012. We ask that you refer to that paper if you use this dataset in your own research publications. Readme: README file Dataset: Release V1 zip archive , or if you prefer a tar file: Release V1 gzipped tar Contact: Robert Collins
Description: We have developed a robust and fast lattice detection algorithm using a probabilistic graph model that has a better detection rate and is approximately 10 times faster than current state-of-the-art algorithms. Project Page: [Deformed Lattice Detection] Source Code/Binaries: Lattice Detection Code for MATLAB Please use this linked citation when using the Deformable Lattice Contact: Christopher Funk, Yanxi Liu
Description: Papercutting is a widespread and ancient artform which, as far as we could tell, had no previous computational treatment. We developed algorithms to analyze the symmetry of papercut patterns and produce efficient folding and cutting plans. Source Code/Binaries: Binary executables are available upon request. Please email the listed contact(s). Sketch: SIGGRAPH 2005 Sketch Contact: Christopher Funk, Yanxi Liu |
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