Journal ArticleDOI
Efficient Human Pose Estimation from Single Depth Images
Jamie Shotton,Ross Girshick,Andrew Fitzgibbon,Toby Sharp,Mat Cook,Mark J. Finocchio,Richard Moore,Pushmeet Kohli,Antonio Criminisi,Alex Aben-Athar Kipman,Andrew Blake +10 more
TLDR
Two new approaches to human pose estimation are described, both of which can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information.Abstract:
We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities.read more
Citations
More filters
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Proceedings ArticleDOI
Learning from Simulated and Unsupervised Images through Adversarial Training
TL;DR: SimGAN as mentioned in this paper uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors, and achieves state-of-the-art results on the MPIIGaze dataset without any labeled real data.
Proceedings ArticleDOI
SUN RGB-D: A RGB-D scene understanding benchmark suite
TL;DR: This paper introduces an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks, and presents a dataset that enables the train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
Book ChapterDOI
Playing for Data: Ground Truth from Computer Games
TL;DR: In this paper, the authors present an approach to rapidly create pixel-accurate semantic label maps for images extracted from modern computer games, which enables rapid propagation of semantic labels within and across images synthesized by the game, without access to the source code or the content.
Journal ArticleDOI
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
TL;DR: A deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF is presented, and a deep structured learning scheme which learns the unary and pairwise potentials of continuousCRF in a unified deep CNN framework is proposed.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Journal ArticleDOI
Mean shift: a robust approach toward feature space analysis
Dorin Comaniciu,Peter Meer +1 more
TL;DR: It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.
Journal ArticleDOI
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.