homework's Introduction
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % This script is part of a application about actions recognition. % Created by Israel Goncalves de Oliveira ([email protected]) for % investigate the performance of multilinear methods to representate the % data and classify. The professor advisor is Jacob Schakanski % ([email protected]). The authors are part of the Singnal Process and % Instrumentation group from PPGEE (Pos-graduacao em Egenharia Eletrica: % http://www.ufrgs.br/ppgee/) and the Pattern Recognition group from PPGC % (Pos-graduacao em Ccomputacao: https://www.inf.ufrgs.br/ppgc/). % % % Versions format: Foo Bar ([email protected]) X.X.DDMMYYYY "A comment". % Israel Oliveira ([email protected]) 1.0.XXXX2017. "The first version" % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This app is about pattern recognition. The mean idea is to compare linear and multilinear methods to recognize a actions perfomed by a person using HDM5 (Muller et all, 2007) database. We use three different datasets from the same database: A) 3D interpoled and normalize motion of the all jonits; B) the 2D version of the A (Ofli et all, 2014) and C) the same B but with all joints (IS NOT FISIABLE!). All results are like results_MDA.mat with R (general results), MC (matrix confusion), num_max (max dimensionality for each dimension) and E (convergence error). The 2D version is obtained Evanglids 2012, angulos das juntas (joint angles). [Muller et all, 2007] About HDM5: <http://www.mpi-inf.mpg.de/resources/HDM05>. If you use and publish results based on this code and data, please cite the following technical report: @techreport{MuellerRCEKW07_HDM05-Docu, author = {Meinard M{\"u}ller and Tido R{\"o}der and Michael Clausen and Bernd Eberhardt and Bj{\"o}rn Kr{\"u}ger and Andreas Weber}, title = {Documentation: Mocap Database {HDM05}}, institution = {Universit{\"a}t Bonn}, number = {CG-2007-2}, year = {2007} } [Ofli et all, 2014] @article{Ofli201424, title = "Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition ", journal = "Journal of Visual Communication and Image Representation ", volume = "25", number = "1", pages = "24 - 38", year = "2014", note = "Visual Understanding and Applications with RGB-D Cameras ", issn = "1047-3203", doi = "http://doi.org/10.1016/j.jvcir.2013.04.007", url = "http://www.sciencedirect.com/science/article/pii/S1047320313000680", author = "Ferda Ofli and Rizwan Chaudhry and Gregorij Kurillo and René Vidal and Ruzena Bajcsy" } A good site: http://www.cad.zju.edu.cn/home/dengcai/Data/DimensionReduction.html It was observed tha GAP method converges to a max performance inherit the incitial random samplpes! See 'prova_search_GAP.mat'. Alternative databases: Berkley MHAD. One dataset using 3D positions (like HDM05) of 43 markers and other using data from 6 accelerometers. http://tele-immersion.citris-uc.org/berkeley_mhad For create the 3rd-order tensor dataset (D) is ready! But for other is important to aply the SMIJ teory. for that we must read and learn more about the joints and 3D-points from de Mocap. This base have a good demos for depth and image view of the motion. Other method: @inproceedings{zhang2016, title={A Large Scale RGB-D Dataset for Action Recognition}, author={Zhang, Jing and Li, Wanqing and Wang, Pichao and Ogunbona, Philip and Liu, Song and Tang, Chang}, booktitle={International Workshop on Understanding Human Activities through 3D Sensors (UHA3DS'16) in conjunction with 23rd International Conference on Pattern Recognition (ICPR2016)}, year={2016}, } MSRAction3DDatasets: http://www.uow.edu.au/~wanqing/#MSRAction3DDatasets HON4D http://www.cs.ucf.edu/~oreifej/HON4D.html See http://vision.imar.ro/human3.6m/description.php See http://mocap.cs.cmu.edu/
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