Title :
DeepPose: Human Pose Estimation via Deep Neural Networks
Author :
Toshev, Alexander ; Szegedy, Christian
Author_Institution :
Google, Mountain View, CA, USA
Abstract :
We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regres- sors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formula- tion which capitalizes on recent advances in Deep Learn- ing. We present a detailed empirical analysis with state-of- art or better performance on four academic benchmarks of diverse real-world images.
Keywords :
neural nets; pose estimation; regression analysis; DNN-based regression problem; DeepPose; academic benchmarks; body joints; deep neural networks; human pose estimation; real-world images; Computational modeling; Detectors; Estimation; Joints; Measurement; Training; Vectors; cascades; deep learning; human pose estimation; neural networks;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
DOI :
10.1109/CVPR.2014.214