Shifting modes: Spectatorship, theatrical virtual reality and motion capture through the experience of <i>Fatherland XR</i>

Smith, Matt

doi:10.1386/vcr_00004_1

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…In the case of a multicamera and a clear outline of the limb without its occlusion, better reconstruction results can be obtained, and the quality of the reconstruction is also related to the number of cameras and their angles. In the case of human movement, the body parts are prone to block each other; if the reconstruction is carried out frame by frame, it will lead to some wrong frame reconstruction results, due to the lack of depth information of the human body posture which is prone to the problem of adhesion of the body parts [15].…”

Section: Human Rigid False Motion Posturementioning

confidence: 99%

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Dai¹

2021

Complexity

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Computer vision is widely used in manufacturing, sports, medical diagnosis, and other fields. In this article, a multifeature fusion error action expression method based on silhouette and optical flow information is proposed to overcome the shortcomings in the effectiveness of a single error action expression method based on the fusion of features for human body error action recognition. We analyse and discuss the human error action recognition method based on the idea of template matching to analyse the key issues that affect the overall expression of the error action sequences, and then, we propose a motion energy model based on the direct motion energy decomposition of the video clips of human error actions in the 3 Deron action sequence space through the filter group. The method can avoid preprocessing operations such as target localization and segmentation; then, we use MET features and combine with SVM to test the human body error database and compare the experimental results obtained by using different feature reduction and classification methods, and the results show that the method has the obvious comparative advantage in the recognition rate and is suitable for other dynamic scenes.

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Section: Human Rigid False Motion Posturementioning

confidence: 99%

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Dai¹

2021

Complexity

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“…Moreover, the image sensor will also have a greater impact on the results of the detection of moving human bodies. The choice of light source in the shooting scene will also have a direct impact on the image preprocessing process [25].…”

Section: Motion Capturementioning

confidence: 99%

Simulation of Gymnastics Performance Based on MEMS Sensor

Chen

Kuang

2021

Preprint

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The development and progress of multi-sensor data fusion theory and method also lay the foundation for the research of human posture tracking system based on inertial sensor. This paper mainly studies the simulation of gymnastic performance based on MEMS sensors. In the preprocessing of reducing noise interference, this paper mainly uses median filter to remove signal burr. In this paper, the use of virtual character model for gymnastics performance. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation and coordinate offset of the limb where each sensor node is located, and realizes the real-time rendering of the virtual human model by using the driver of the human model. At the same time, it controls the storage of sensor data, the driving of model and the display of graphical interface. When the gesture action is about to happen, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of MEMS sensor. When the gesture action is completed, a signal to end the action is given to the system to mark the end of the action, so that the original signal data between the beginning and end of the gesture action can be captured. In order to ensure the normal communication between PS and PL, it is necessary to test the key interface. Because the data received by the SPI acquisition module is irregular, it is unable to verify whether the data is wrong. Therefore, the SPI acquisition module is replaced with an automatic incremental data module, and it is generated into an IP core to build a test platform for testing. The data show that the average measurement errors of x-axis displacement, Y-axis displacement, z-axis displacement and three-dimensional displacement are 8.17%, 7.51%, 9.72% and 8.7%, respectively. The results show that the MEMS sensor can accurately identify the action with high accuracy.

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Section: Motion Capturementioning

confidence: 99%

Simulation of gymnastics performance based on MEMS sensor

Chen

Kuang

2021

EURASIP J. Adv. Signal Process.

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The development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node’s limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy.

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Shifting modes: Spectatorship, theatrical virtual reality and motion capture through the experience of Fatherland XR

Cited by 3 publications

References 6 publications

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Simulation of Gymnastics Performance Based on MEMS Sensor

Simulation of gymnastics performance based on MEMS sensor

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