Local Riesz Pyramid for Faster Phase-Based Video Magnification

Takeda, Shoichiro; Isogai, Megumi; Shimizu, Shohei; Kimata, Hideaki

doi:10.1587/transinf.2020pcp0006

Cited by 6 publications

(4 citation statements)

References 17 publications

(92 reference statements)

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“…In the bone motion sequence, the position information of the important joints of the human body is recorded in each frame, which is generally expressed in Cartesian coordinates relative to the acquisition equipment. In the process of human movement, in addition to the extension, flexion, and lifting of parts, it also includes the rotation of limbs and trunk [13].…”

Section: Get the Human Skeleton Frame Of Tai Chi Chuanmentioning

confidence: 99%

Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

Liu

Qing

Chen

et al. 2022

Mathematical Problems in Engineering

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The current action recognition method has good effect when applied to static recognition, but, when applied to dynamic action sequence recognition, the temporal and spatial feature segmentation is too dependent on sample template, resulting in low recognition accuracy. To address the inadequacies of standard movement detection techniques in the application of comparable domains, a deep learning algorithm is utilised to recognise Tai Chi Chuan motions. For Tai Chi Chuan movement human body skeleton framework, add image depth parameter is added, and OpenPose model is utilised to estimate joint point coordinates. The ST-GCN deep learning model was created to recognise Tai Chi Chuan motions by extracting movement features from the spatiotemporal trajectory of human joints during Tai Chi Chuan movements. Instance test results show that rate of using the deep learning algorithm of gesture recognition is 89.22%, with significantly lower error detection rate, which is good for Tai chi chuan movement recognition effect.

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Section: Get the Human Skeleton Frame Of Tai Chi Chuanmentioning

confidence: 99%

Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

Liu

Qing

Chen

et al. 2022

Mathematical Problems in Engineering

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“…In this way, the pixel value of each position in the original image can be replaced by the mean value solved by the filter template, which is the basic principle in the application of mean filtering. The formula is as follows [11]:…”

Section: Research On Rbf-based 3d Visual Detectionmentioning

confidence: 99%

RBF-Based 3D Visual Detection Method for Chinese Martial Art Wrong Movements

Wang

Pan

et al. 2022

Wireless Communications and Mobile Computing

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The accuracy of action detection is limited by the extracted action, and there are problems of high processing complexity and low efficiency. Therefore, a three-dimensional visual detection method of martial art wrong action based on RBF is proposed. After noise reduction and weighting processing of martial art action video images, a martial art action 3D visual transformation model is established. According to the 3D visual model, C3D features are used to represent martial art actions. The video is segmented using sparse coding to determine the detection range. RBF neural network model is established, and the combination of the above 3D visual model and network parameters is obtained by sample training to detect martial art wrong actions. The test method of the experimental results shows the detection of the research under the condition of different degrees of precision, an average of at least 5%, and the method of detection of high efficiency and stability.

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“…[1]- [3] The camera-based measurement method is unaffected by loading and temperature errors and has the advantage of obtaining a full-field image. [4]- [7] Nevertheless, data acquisition from video frames is challenging when the vibration motion tracked in video images is as tiny as subpixel; however, small vibrations can be amplified using motion magnification algorithms [8]- [13] and can be applied to vibration measurements [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the fact that CSP has a long computation time due to overcompleteness and a computational disadvantage [11], [17], it has substantial advantages in terms of obtaining accurate magnified motion and in detecting motion in many directions. Therefore, a method for reducing computing time by employing the Riesz pyramid, instead of CSP, has been proposed [11] for calculating faster using the local Riesz pyramid approach [13]. However, compared to the CSP, the Riesz pyramid has a disadvantage in detecting motion in various directions, and the local Riesz pyramid has a limitation in that motion is weakly magnified.…”

Section: Introductionmentioning

confidence: 99%

Phase-Based Motion Magnification for Structural Vibration Monitoring at a Video Streaming Rate

Lee

2022

IEEE Access

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Here we propose a novel approach to image magnification that enables frame-by-frame motion magnification application at a video streaming rate of around 30 fps. This novel approach can be used instead of batch processing an image file in conventional phase-based magnification (PBM). This new PBM method can instantly show a magnified video streaming on display at 30 fps, which is helpful for vibration measurement and for monitoring tasks where magnified images need to be viewed simultaneously.To accomplish video streaming-rate magnification, the proposed PBM uses time-domain convolution in temporal bandpass filtering for frame-by-frame operation whereas conventional PBM employs a frequencydomain filter that is applied to the entire image file at once. An experiment was conducted to monitor the vibration of a cantilever using a webcam streaming at the same frame rate, and data were collected simultaneously using a laser Doppler vibrometer for comparison. The experiment confirmed that the proposed PBM approach is more effective than the conventional magnification method and it also analyzed the system performance for vibration measurement. Additionally, the proper orthogonal mode could be found through the singular value decomposition from vibration displacement data of the cantilever that was collected instantly from the magnified image frames. Furthermore, the dominant mode could be effectively extracted from excitation at the resonance frequency. Because of magnification factors, the vibration displacements from the proposed method were estimated using linear regression and the accuracy of the estimated displacements was within the permissible error bound. Since the proposed PBM is a frame-byframe operation, instantaneous adjustment of magnification parameters is available, even while the magnification is being processed. In addition, the proposed PBM is independently adjustable to the number of image frames for temporal FIR filter order.

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Local Riesz Pyramid for Faster Phase-Based Video Magnification

Cited by 6 publications

References 17 publications

Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

RBF-Based 3D Visual Detection Method for Chinese Martial Art Wrong Movements

Phase-Based Motion Magnification for Structural Vibration Monitoring at a Video Streaming Rate

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