Omnidirectional 3D Point Clouds Using Dual Kinect Sensors

Yun, Seok Min; Choi, Jaewon; Won, Chee Sun

doi:10.1155/2019/6295956

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…In this paper, the rigid point cloud registration was carried out on the front and back human point clouds obtained by the above segmentation, and 3D human body imaging with high accuracy could be obtained efficiently and conveniently without using checkerboard or other calibration equipment.The ICP algorithm is the most widely used algorithm in rigid point cloud registration [11][12] . Due to the two point clouds of front and back have no overlapping regions, it is difficult to apply ICP directly [13] . In order to solve this problem, Seokmin Yun et al proposed a registration scheme based on 3D human skeleton information.…”

Section: Gallery Set Data Preprocessingmentioning

confidence: 99%

Multi-view gait recognition based on point cloud gallery sets

Li¹,

Tang²,

Guo³

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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As a biometric recognition technology with the advantages of long-distance and anti-camouflage, gait recognition has great application value in public security criminal investigation, security monitoring and other fields. Due to the influence of external factors such as viewing angle, clothing and light on the appearance of gait sequence, most of the current gait recognition algorithms have low accuracy and cannot be applied on a large scale. Aiming at the view angle problem in gait recognition, we proposed a method of constructing full-view gait gallery set based on dual Kinect cameras and edge point cloud registration. Then, we used the RGB camera to collect gait sequence from any angle of view (including depression angle), and designed a gait feature matching network called GaitCrossNet, which is used to match the RGB gait contour sequence to the sequence projected from point cloud sets. The experimental results showed that the average recognition accuracy of the algorithm proposed in this paper can reach 85.2%, which has obvious advantages compared with the existing recognition schemes.

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Section: Gallery Set Data Preprocessingmentioning

confidence: 99%

Multi-view gait recognition based on point cloud gallery sets

Li¹,

Tang²,

Guo³

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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“…First up was the identification of the left-hand gestures. The Kinect sensor was adopted to track the human skeleton [23][24][25]. Second, the left-hand joints were detected.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

The Manipulation of Real-Time Kinect-Based Robotic Arm Using Double-Hand Gestures

et al. 2020

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This study focused on utilizing the Kinect depth sensor to track double-hand gestures and control a real-time robotic arm. The control system is mainly composed of the microprocessor, a color camera, the depth sensor, and the robotic arm. The Kinect depth sensor was used to take photos of the human body to analyze the skeleton of a human body and obtain the relevant information. Such information was used to identify the gestures of the left hand and the left palm of the user. The gesture of left hand was used as an input command device. The gesture of the right hand was used for imitation movement teaching of robotic arm. From the depth sensor, the real-time images of the human body and the deep information of each joint were collected and converted to the relative positions of the robotic arm. Combining forward kinematics and inverse kinematics and D-H link, the gesture information of the right hand was calculated, which was converted via coordinates into each angle of the motor of the robotic arm. From the color camera, when the left palm was not detected, the user could simply use the right hand to control the action and movement of the real-time robotic arm. When the left palm was detected and 5 fingertips were identified, it meant the start of recording the real-time imitation movement of the robotic arm by the right hand. When 0 fingertip was identified, it meant the stoppage of the above recording. When 2 fingertips were identified, the user could not only control the real-time robotic arm but also repeat the recorded actions.

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“…In Refs. [17,18], the authors used various kinect sensors (KS) to present a technique of body movement analyses in dance. To solve occlusion and self-occlusion problem monitoring to improve the robustness of skeletal tracking, fusion is the approach proposed.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning model-based two-dimensional matrix computation model for human motion and dance recovery

Zhang

2020

Complex Intell. Syst.

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Many regions of human movement capturing are commonly used. Still, it includes a complicated capturing method, and the obtained information contains missing information invariably due to the human's body or clothing structure. Recovery of motion that aims to recover from degraded observation and the underlying complete sequence of motion is still a difficult task, because the nonlinear structure and the filming property is integrated into the movements. Machine learning model based two-dimensional matrix computation (MM-TDMC) approach demonstrates promising performance in short-term motion recovery problems. However, the theoretical guarantee for the recovery of nonlinear movement information lacks in the two-dimensional matrix computation model developed for linear information. To overcome this drawback, this study proposes MM-TDMC for human motion and dance recovery. The advantages of the machine learning-based Two-dimensional matrix computation model for human motion and dance recovery shows extensive experimental results and comparisons with auto-conditioned recurrent neural network, multimodal corpus, low-rank matrix completion, and kinect sensors methods.

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Omnidirectional 3D Point Clouds Using Dual Kinect Sensors

Cited by 8 publications

References 28 publications

Multi-view gait recognition based on point cloud gallery sets

Multi-view gait recognition based on point cloud gallery sets

The Manipulation of Real-Time Kinect-Based Robotic Arm Using Double-Hand Gestures

Machine learning model-based two-dimensional matrix computation model for human motion and dance recovery

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