Real-time identification and localization of body parts from depth images

By Christian Plagemann, Varun Ganapathi, Daphne Koller, Sebastian Thrun. 2010

339 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “[62] introduced an approach to recognize body parts using Haar features [84] and construct a skeleton model on these parts.”
Efficient Human Pose Estimation from Single Depth Images (2013, background): “BPC, originally published in [14], was inspired by recent object recognition work that divides objects into parts (e.g., [15], [16], [17], [5]).”
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].”
Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016, background): “[31,32]) have become computationally cheap and readily available at real-time performance on conventional computers.”
Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation (2016, background): “Their corresponding Gaussian kernel density maps are depicted in the 2nd row, followed by the pose estimation results in the 3rd row. training [4], [5].”
Functional categorization of objects using real-time markerless motion capture (2011, background): “While these works rely on local optimization, which makes them prone to errors, Ganapathi et al .[ 12] propose using body part detectors [ 23 ] for full body motion capture.”
Human Action Recognition From Various Data Modalities: A Review (2020, neutral): “[167], [168] and depth data [169], [170], [171], [172], [173], [174].”
Real time motion capture using a single time-of-flight camera (2010, methodology): “Discriminatively trained classifiers are applied to patches centered on the points to determine to what body part class they belong to. If the classifier is sufficiently confident, the feature is reported as a detection (see [ 11 ] for details).”
Putting the pieces together: Connected Poselets for human pose estimation (2011, methodology): “Depth information has been used before as the basis of static pose estimation with a heuristics approach [18], model based approach [30], a part-based approach with bayesian inference [31], a hybrid model and example-based approach [14] and the object category recognition approach of Shotton et al [”
Human detection using depth information by Kinect (2011, background): “Plagemann et al. [10] use a novel interest point detector to solve the problem of detection and identifying body parts in depth images.”
Informative joints based human action recognition using skeleton contexts (2015, methodology): “Inspired by the instinct of the human vision system, we analyze the mean contributions of human joints for each action class via differential entropy of the joint locations.”
Efficient regression of general-activity human poses from depth images (2011, background): “A common theme in many recent approaches for human pose estimation has been the focus on localizing different body parts [2] [16] [18].”
Realtime and Robust Hand Tracking from Depth (2014, background): “Part detection and part-based initialization have been proven critical to the robustness in body tracking [18, 1, 30, 24].”