A hybrid framework for automatic joint detection of human poses in depth frames

Kong, Longbo; Yuan, Xiaohui; Maharjan, Amar

doi:10.1016/j.patcog.2017.12.020

Cited by 27 publications

(11 citation statements)

References 12 publications

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“…We believe that the success rate of identifying the correct joint will be tremendously improved, in cases when the detection of the waists is optimized. [22]) and (Kong et al [11]), respectively.…”

Section: Experiments and Resultsmentioning

confidence: 96%

“…To further prove the feasibility of the proposed algorithm, the overall accuracy of joints is calculated as listed in Table I. Compared with Kong et al [11] and Shotton et al [22], we set 6 cm and 10 cm respectively as a reasonable threshold to judge whether the joint is detected. When a joint within 6 cm is chosen as the threshold in Table I, the overall accuracy is decreased.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Compared with the whole image search method, this method not only improves the search speed, but also avoids falling into the local minimum of the algorithm. Kong et al [11] presented a hybrid framework for automatic joint detection using a depth camera. By constraining the geodesic distance between every two joints, implicit and dominant joints can be detected.…”

Section: Related Workmentioning

confidence: 99%

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3D Joints Estimation of the Human Body in Single-Frame Point Cloud

Wang

et al. 2020

IEEE Access

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Joint estimation of human body in point cloud is a key step for tracking human movements. In this work, we present a geometric method to achieve detection of the joints from a single-frame point cloud captured using a Time-of-Flight (ToF) camera. Three-dimensional (3D) human silhouette, as global feature of the single-frame point cloud, is extracted based on the pre-processed data, the angle and aspect ratio of the silhouette are subsequently utilized to perform pose recognition, and then 14 joints of human body are derived via geometric features of 3D silhouette. To verify this method, we test on an in-house captured 3D dataset containing 1200-frame depth images, which can be categorized into four different poses (upright, raising hands, parallel arms, and akimbo). Furthermore, we test on a subset of the G3D dataset. By hand-labelling the joints of each human body as the ground truth for validation and benchmarks, the average normalized error of our geometric method is less than 5.8 cm. When the distance threshold from the ground truth is 10 cm, the results demonstrate that our proposed method delivers improved performance with an average accuracy in the range of 90%. INDEX TERMS Depth camera, human pose detection, joint detection, sensor systems and applications.

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Section: Experiments and Resultsmentioning

confidence: 96%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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3D Joints Estimation of the Human Body in Single-Frame Point Cloud

Wang

et al. 2020

IEEE Access

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“…This work proposed a method for fall detection by analyzing tracked key body joints of subject using a depth-camera. There are several researches to detect body joints position in the human body using a Kinect based on depth image [14,15]. Amongst important body positions, head position was reported as the first one that could suffer the most impact.…”

Section: Related Workmentioning

confidence: 99%

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

Watanapa

Patsadu

Dajpratham

et al. 2018

Applied Computational Intelligence and Soft Computing

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This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond.

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“…Recently, owing to extracted depth information, depth cameras [2,3] are applied for estimating 3D human poses and representing human activity. Kong et al [4] presented a hybrid framework to detect joints automatically based on a depth camera. Then, 3D human poses were estimated using the located human skeleton model.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Three-Dimensional Human Poses: A Combined Approach of Iterative Calculation on Skeleton Model and Conformal Geometric Algebra

Huang

Gao

2019

Symmetry

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Reconstructing three-dimensional (3D) human poses is an essential step in human bodyanimation. The purpose of this paper is to fill the gap in virtual reality research by reconstructingpostures in a high-precision human model. This paper presents a new approach for 3D human posereconstruction based on the iterative calculation of a skeleton model and conformal geometric algebra,captured by a monocular camera. By introducing the strip information of clothes and prior data ofdifferent human limbs, the location of joint points on the human body will not be affected by theocclusion problem. We then calculate the 3D coordinates of joint points based on the proposed methodof the iterative calculation of the skeleton model, which can solve the high-cost problem caused by theneed for multiple cameras or a depth camera. Subsequently, we utilize high-performance conformalgeometric algebra (CGA) in relation to rotation transformations in order to improve the adjustmentof the postures of the human limbs. Finally, realistic 3D human poses are reconstructed—specifically,the motion of the human limbs—using a rigid transformation of CGA and a smooth connection ofthe limb parts based on a high-precision model. Compared with the existing methods, the proposedapproach can obtain satisfactory and realistic 3D human pose estimation results using grid models.

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A hybrid framework for automatic joint detection of human poses in depth frames

Cited by 27 publications

References 12 publications

3D Joints Estimation of the Human Body in Single-Frame Point Cloud

3D Joints Estimation of the Human Body in Single-Frame Point Cloud

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

Reconstructing Three-Dimensional Human Poses: A Combined Approach of Iterative Calculation on Skeleton Model and Conformal Geometric Algebra

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