Nonlinear Body Pose Estimation from Depth Images

By Daniel Grest, J. Woetzel, Reinhard Koch. 2005

138 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jitendra Malik
3. Ming-Hsuan Yang
4. Andrew Zisserman
5. Dacheng Tao
6. Xiaogang Wang
7. Trevor Darrell
8. Alan L. Yuille

In Control and Systems Engineering

1. Dieter Fox
2. Gyeongsik Moon
3. Kyoung Mu Lee
4. Jamie Shotton
5. William A. Hoff
6. Pushmeet Kohli
7. Brian Reily
8. Fei Han

Citations in the corpus

• Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “[57] ICP using optimized Jacobian Single depth image 3D skeleton, over 10 fps”
• V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map (2017, methodology): “The ICP algorithm is commonly used for 3D body tracking [7, 12, 17, 21].”
• Conditional regression forests for human pose estimation (2012, background): “The availability of high-speed depth sensors has led to tremendous progress in human pose estimation [13, 15, 30, 25, 11].”
• Real-time human pose recognition in parts from single depth images (2011, background): “The second intermediate representation is a list of joint hypotheses Jt, which contains triples of (body part, 3D position, confidence) hypotheses, with say five hypothe­ses per body part.”
• DART: Dense Articulated Real-Time Tracking (2014, background): “Grest et al. [13] presented early work on human body tracking from depth, but subsampled the points and still did not reach real-time.”
• Survey on Emotional Body Gesture Recognition (2018, methodology): “Hash-initialized skeletons were tracked through range images using the Iterative Closest Point (ICP) algorithm in [67].”
• Real-Time Human Pose Tracking from Range Data (2012, unknown): “The tracking accuracy is significantly higher than for the other approaches – especially for hard-to-estimate joint locations, such as, the elbows. camera, (b) to show that the system outperforms Articulated ICP [12] [16] and existing ray-casting based approaches [19] on their evaluation data set in”
• Efficient Human Pose Estimation from Single Depth Images (2013, methodology): “Traditional human body tracking algorithms [8], [9], [1], [10], [6], [11] infer a complete skeleton by exploiting kinematic constraints and can achieve high frame-rates by using temporal coherence from frame-to-frame.”