Human action data set generation in a machine learning system

作者: Hamidreza Vaezi Joze , Mehran Khodabandeh , Vivek Pradeep , Ilya Zharkov

DOI:

关键词: Artificial intelligenceBackground imageSkeleton (category theory)Computer scienceImage (mathematics)Frame (networking)Reference imageSet (abstract data type)Data setMachine learningAction (philosophy)

摘要: Methods, apparatuses, and computer-readable mediums for generating human action data sets are disclosed by the present disclosure. In an aspect, apparatus may receive a set of reference images, where each images within includes person, background image. The identify body parts person from image generate transformed skeleton mapping to corresponding target skeleton. mask generate, using machine learning, frame formed according

参考文章(66)
Amir Roshan Zamir, Khurram Soomro, Mubarak Shah, UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild arXiv: Computer Vision and Pattern Recognition. ,(2012)
Kensuke Yokoi, Hiroki Nishimura, Ikuro Sato, APAC: Augmented PAttern Classification with Neural Networks. arXiv: Computer Vision and Pattern Recognition. ,(2015)
Wonpil Yu, Jae-Yeong Lee, Michael Sahngwon Ryoo, Method for human activity prediction from streaming videos ,(2012)
Arthur Szlam, Emily Denton, Rob Fergus, Soumith Chintala, Deep generative image models using a Laplacian pyramid of adversarial networks neural information processing systems. ,vol. 28, pp. 1486- 1494 ,(2015)
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri, Learning Spatiotemporal Features with 3D Convolutional Networks 2015 IEEE International Conference on Computer Vision (ICCV). pp. 4489- 4497 ,(2015) , 10.1109/ICCV.2015.510
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, C. Lawrence Zitnick, Microsoft COCO: Common Objects in Context Computer Vision – ECCV 2014. pp. 740- 755 ,(2014) , 10.1007/978-3-319-10602-1_48
Philipp Fischer, Thomas Brox, None, U-Net: Convolutional Networks for Biomedical Image Segmentation medical image computing and computer assisted intervention. pp. 234- 241 ,(2015) , 10.1007/978-3-319-24574-4_28
Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera, Albert Clapes, Kamal Nasrollahi, Michael B. Holte, Thomas B. Moeslund, Keep it accurate and diverse: Enhancing action recognition performance by ensemble learning computer vision and pattern recognition. pp. 22- 29 ,(2015) , 10.1109/CVPRW.2015.7301332
Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici, Beyond short snippets: Deep networks for video classification computer vision and pattern recognition. pp. 4694- 4702 ,(2015) , 10.1109/CVPR.2015.7299101
Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Trevor Darrell, Kate Saenko, Long-term recurrent convolutional networks for visual recognition and description computer vision and pattern recognition. pp. 2625- 2634 ,(2015) , 10.1109/CVPR.2015.7298878