Human pose estimation from a single view point, real-time range sensor

Siddiqui, Matheen; Medioni, Gérard

doi:10.1109/cvprw.2010.5543618

Cited by 76 publications

(36 citation statements)

References 21 publications

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“…Zhu and Fujimura [18] combine articulated ICP with part detection for improved robustness. Ganapathi et al [19] and Siddiqui et al [20] approach the markerless people tracking problem from a hypothesize-and-test angle, with hypotheses additionally generated from part detectors. Ganapathi et al implement this using a GPU-accelerated generative model for synthesizing and evaluating large sets of hypothetical body configurations.…”

Section: Related Workmentioning

confidence: 99%

“…As the right diagram in Fig. 2 illustrates, approaches using ray casting [19,20] often make use of the hypothesize-and-test paradigm, that is, they sample model configurations, synthesize the respective depth measurements and compare these to the actual measurements. Such a system is typically hard to optimize, because the direction of improvement of the optimization objective has to be estimated indirectly from samples in a high-dimensional space.…”

Section: Measurement Modelmentioning

confidence: 99%

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Real-Time Human Pose Tracking from Range Data

Ganapathi

Plagemann

Koller

et al. 2012

Lecture Notes in Computer Science

116

124

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Abstract. Tracking human pose in real-time is a difficult problem with many interesting applications. Existing solutions suffer from a variety of problems, especially when confronted with unusual human poses. In this paper, we derive an algorithm for tracking human pose in real-time from depth sequences based on MAP inference in a probabilistic temporal model. The key idea is to extend the iterative closest points (ICP) objective by modeling the constraint that the observed subject cannot enter free space, the area of space in front of the true range measurements. Our primary contribution is an extension to the articulated ICP algorithm that can efficiently enforce this constraint. Our experiments show that including this term improves tracking accuracy significantly. The resulting filter runs at 125 frames per second using a single desktop CPU core. We provide extensive experimental results on challenging real-world data, which show that the algorithm outperforms the previous state-of-the-art trackers both in computational efficiency and accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Measurement Modelmentioning

confidence: 99%

Real-Time Human Pose Tracking from Range Data

Ganapathi

Plagemann

Koller

et al. 2012

Lecture Notes in Computer Science

116

124

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show abstract

“…The task has recently been greatly simplified by the introduction of realtime depth cameras [16,19,44,37,28,13]. However, even the best existing systems still exhibit limitations.…”

Section: Introductionmentioning

confidence: 99%

Real-time human pose recognition in parts from single depth images

et al. 2011

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We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem. Our large and highly varied training dataset allows the classifier to estimate body parts invariant to pose, body shape, clothing, etc. Finally we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.The system runs at 200 frames per second on consumer hardware. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.

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“…As Microsoft's Kinect which can measure the depth information has been released in the late 2010, the studies using a depth value to recognize the user's motion have been presented [6,7,8]. In addition, the studies have been presented about the methods for providing an event with the user's motion that is obtained by using a depth camera [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Touch Pen Using Depth Information

Lee¹,

Kwon²

2015

Journal of Korea Multimedia Society

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Current touch pen requires the special equipments to detect a touch and its price increases in proportion to the screen size. In this paper, we propose a method for detecting a touch and implementing a pen using the depth information. The proposed method obtains a background depth image using a depth camera and extracts an object by comparing a captured depth image with the background depth image. Also, we determine a touch if the depth value of the object is the same as the background and then provide the pen event. Using this method, we can implement a cheaper and more convenient touch pen.

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Human pose estimation from a single view point, real-time range sensor

Abstract: Abstract

Cited by 76 publications

References 21 publications

Real-Time Human Pose Tracking from Range Data

Real-Time Human Pose Tracking from Range Data

Real-time human pose recognition in parts from single depth images

Touch Pen Using Depth Information

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