TASVIRLARDAGI ASOSLANGAN INSON HARAKATINI ANIQLASH OPENPOSE YORDAMIDA ALGORITMI
Article Sidebar
Main Article Content
Аннотация:
Mazkur tadqiqot OpenPose vositasidan insonning kalit nuqtalarini generatsiya qilish uchun foydalangan holda, suratlar asosida inson harakatlarini aniqlovchi tizimni taqdim etadi. RGB-ga asoslangan usul tasvirni qayta ishlash bo‘yicha oldingi bilimlardan foydalanib, yuqori aniqlikdagi harakatni aniqlash imkonini beradi, lekin hisoblash quvvati va saqlash resurslariga yuqori talablar qo‘yadi, shuningdek, fon shovqinlari va yorug‘lik o‘zgarishlariga sezgir. Aksincha, skeletga asoslangan usul kamroq hisoblash resurslari talab qiladi va yorug‘lik yoki fon ta’siridan kamroq ta’sirlanadi, ammo kontekst ma’lumotlarining yetishmasligi tufayli cheklovlarga ega.
Article Details
Как цитировать:
Библиографические ссылки:
A Robust Human Activity Recognition Approach Using OpenPose, Motion Features, and Deep Recurrent Neural Network Farzan Majeed Noori1(B) , Benedikte Wallace1,2 , Md. Zia Uddin1 , and Jim Torresen1,2.
Graph transformer network with temporal kernel attention for skeleton-based action recognition Yanan Liu, Hao Zhang, Dan Xu ∗ , Kangjian He
E. Insafutdinov et al., “DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2016.
S.-E. Wei et al., “Convolutional Pose Machines,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016.
K. He et al., “Mask R-CNN,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017.
Z. Cao et al., “Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017.
E. Insafutdinov et al., “DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2016.
G. Moon et al., “V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018.
OpenPose GitHub Repository, https://github.com/CMU-Perceptual-Computing-Lab/openpose.
B. Xiao et al., “Simple Baselines for Human Pose Estimation and Tracking,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018.
H. Joo et al., “Panoptic Studio: A Massively Multiview System for Social Motion Capture,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2015.
J. Liu et al., “NTU RGB+D: A Large-Scale Dataset for 3D Human Activity Analysis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016.
A. F. Bobick and J. W. Davis, “The Recognition of Human Movement Using Temporal Templates,” IEEE Trans. Pattern Anal. Mach. Intell., 2001.
S. Patel et al., “A Review of Wearable Sensors and Systems with Application in Rehabilitation,” J. Neuroeng. Rehabil., 2012.
R. Poppe, “A Survey on Vision-Based Human Action Recognition,” Image Vis. Comput., 2010.
M. Zyda, “From Visual Simulation to Virtual Reality to Games,” Computer, 2005.