scholarly journals Volleyball Action Extraction and Dynamic Recognition Based on Gait Tactile Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Limin Qi
Keyword(s):  
Human Body ◽  
Difference Method ◽  
Body Position ◽  
Capture Rate ◽  
Detection Algorithm ◽  
Human Motion ◽  
Position Estimation ◽  
Test Method ◽  
Frame Difference ◽  
Background Segmentation

At present, the industry research of volleyball technology is relatively in-depth, and the analysis of the muscle strength characteristics and coordination of the jumping ball is less, which is not conducive to the control of technical movements. This study used a wireless portable surface EMG tester (16 lines) to analyze the EMG of the main muscle groups in athletes’ volleyball and conducted a video synchronization test method to find the position of the human body. Therefore, a background-based frame difference method is proposed to detect the position and obtain the precise position of the human body. Experiments show that the background-based three-frame difference method effectively eliminates the “hole” effect of the original three-frame difference method and provides an accurate and complete framework for identifying the human body. Adjust the recognition frame according to the proportion of the human body in the image, and use the predefined parameters of the severe frame to perform forward/volleyball background segmentation. The novelty of this document lies in the completion of the complete human body placement of the above three tasks, precapture/background segmentation, and an improved human body position estimation algorithm to extract the human body pose from the video. First, locate the human body in each frame of the video, and then, perform the process of estimating the position of the graphic model based on the color and texture of the unit. After recognizing the gesture of each image in the video, the recognition result will be displayed. Experiments show that after detecting the position of the human body, the predefined frame setting process of the tomb is carried out in two steps, which improves the automation of the human body image detection algorithm, effectively extracts the human motion video, and increases the motion capture rate by more than 30%, to provide a useful reference for the improvement of college volleyball players’ movement skills and training competitions.

scholarly journals A Specific Algorithm Based on Motion Direction Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhesen Chu ◽  
Min Li
Keyword(s):  
Human Body ◽  
Body Position ◽  
Motion Vector ◽  
Human Motion ◽  
Generative Model ◽  
Experimental Results ◽  
Body Motion ◽  
Motion Vectors ◽  
Motion Direction ◽  
Current Frame

In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.

A Moving Object Detection Algorithm Based on ORB under Dynamic Scene

2014 ◽  
Vol 602-605 ◽  
pp. 1638-1641 ◽  
Author(s):  
Wen Hao Luo
Keyword(s):  
Object Detection ◽  
Difference Method ◽  
High Speed ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Transformation Model ◽  
Dynamic Scene ◽  
Frame Difference ◽  
Inter Frame

In this thesis, a moving object detection algorithm under dynamic scene is proposed, which is based on ORB feature. Firstly, we extract feature points and match them by using ORB. We then obtain global motion compensation image by parameters of transformation matrix based on the RANSAC method. Finally, we use the inter-frame difference method to achieve the detection of moving targets. The high speed and accuracy of ORB feature point matching method, as well as the effectiveness of the RANSAC method for removing outliers ensure accurate calculation of parameters of affine transformation model. Combined with inter-frame difference method, foreground objects can be detected entirely. Experiment results show that the algorithm can accurately detect moving objects, and to some extent, it can solve the issue of real-time detection.

Human motion analysis and action scoring technology for sports training based on computer vision features

2021 ◽  
pp. 1-9
Author(s):  
Yong Bai ◽  
Yinggang Chen
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Digital Image Processing ◽  
Human Body ◽  
Median Filter ◽  
Human Movement ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Recognition Model ◽  
Frame Difference

With the advent of the information age, computer-related application research has become more and more extensive, human motion analysis and action scoring based on computer vision have gradually become the focus of attention. In order to adapt to the development of the times and solve the problems related to the analysis of human motion, the experiment analyzed the similarity of eight common human movement behaviors, analyze the movement speed of men and women under sports training, and analyzed the accuracy of the human body motion recognition model in the two cases of the original gray data and the frame difference channel, finally, the denoising performance of four different algorithms of SMF, EMF, RAMF and median filter algorithm in digital image processing is analyzed. The final result shows that there is a big similarity between the same kind of human movement behavior, the accuracy rate of the frame difference channel human body recognition model is higher than that of the original gray data recognition model, and digital image processing median filter algorithm has good image denoising performance.

scholarly journals Deep Learning-Driven Gaussian Modeling and Improved Motion Detection Algorithm of the Three-Frame Difference Method

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dingchao Zheng ◽  
Yangzhi Zhang ◽  
Zhijian Xiao
Keyword(s):  
Deep Learning ◽  
Motion Detection ◽  
Adaptive Learning ◽  
Difference Method ◽  
Gaussian Model ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Frame Difference ◽  
Gaussian Modeling ◽  
Difference Calculation

To enhance the effect of motion detection, a Gaussian modeling algorithm is proposed to fix holes and breaks caused by the conventional frame difference method. The proposed algorithm uses an improved three-frame difference method. A three-frame image sequence with one frame interval is selected for pairwise difference calculation. The logical “OR” operation is used to achieve fast motion detection and to reduce voids and fractures. The Gaussian algorithm establishes an adaptive learning model to make the size and contour of the motion detection more accurate. The motion extracted by the improved three-frame difference method and Gaussian model is logically summed to obtain the final motion foreground picture. Moreover, a moving target detection method, based on the U-Net deep learning network, is proposed to reduce the dependency of deep learning on the number of training datasets. It helps the algorithm to train models on small datasets. Next, it calculates the ratio of the number of positive and negative samples in the dataset and uses the reciprocal of the ratio as the sample weight to deal with the imbalance of positive and negative samples. Finally, a threshold is set to predict the results for obtaining the moving object detection accuracy. Experimental results show that the algorithm can suppress the generation and rupture of holes and reduce the noise. Also, it can quickly and accurately detect movement to meet the design requirements.

scholarly journals Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Renzheng Xue ◽  
Ming Liu ◽  
Xiaokun Yu
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Moving Objects ◽  
Target Location ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Moving Target ◽  
Size Change ◽  
Detection And Tracking ◽  
Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

Design and Realization of a Wearable Hip-Airbag System for Fall Protection

2013 ◽  
Vol 461 ◽  
pp. 667-674 ◽  
Author(s):  
Qi Zhang ◽  
Hui Qi Li ◽  
Yun Kun Ning ◽  
Ding Liang ◽  
Guo Ru Zhao
Keyword(s):  
Human Body ◽  
Inertial Sensor ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Human Motion ◽  
The Body ◽  
Needle Valve ◽  
Sensor Module ◽  
Fall Protection ◽  
The Impact

Nowadays falls are a serious problem for elderly people with the coming of aged society in the world. According to statistics,hip fracture accounts for the most of the deaths and costs of all the fall-induced injury. This paper presented an airbag system of hip protection, which included air source, sensors, microcontroller, gas circuit and airbags. A six-axial inertial sensor module that integrated an embedded three-axis MEMS accelerometer and three-axis MEMS gyroscope was used to collect human motion data, and a one-axis obliquity sensor was used to collect human angle data. The microcontroller was employed to recognize the activities of daily living (ADL) and falls based on fall detection algorithm and the collected data from sensors. The gas circuit was triggered once the event that the fall would be inevitable was confirmed by the microcontroller, then the compressed gas would fill into airbags through the gas circuit rapidly. Therefore, a buffer would appear between the human body and the ground before the body impacting the ground, which would reduce the impact of the human body. Compressed CO2 was selected as the air source after we tested several kinds of gas. A 16g CO2 pressurized cylinder could provide enough pressure and volume to inflate quickly the airbags. In order to improve the reliability of the gas circuit, a needle valve was optimized from the several designed structures by the experimental optimization methods. Finally, the airbag system was tested in various designed trials. The results indicated that the system gained the satisfaction for the design requirements and would be potential to apply to the protection of hip joint in the fall high-risk people in the future.

scholarly journals Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Limin Qi ◽  
Yong Han
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Capture Rate ◽  
Detection Algorithm ◽  
Human Motion ◽  
Contour Detection ◽  
Image Resolution ◽  
Detection Accuracy ◽  
Image Size ◽  
Learning Network

To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. Firstly, the perception ability of deep learning is used to match human motion feature points to obtain human motion posture features. Secondly, normalize the human motion image, take the color histogram distribution of human motion posture as the antigen, search the region close to the motion posture in the image, and take its candidate region as the antibody. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Finally, using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. The results show that when the image resolution is 384 × 256 px, the motion pose contour detection accuracy of this algorithm is 87%. When the image size is 30 MB, the recognition time of this method is only 0.8 s. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. This shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance.

Human body position estimation system using electric field resonance coupling

ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Shimon Ajisaka ◽  
Sousuke Nakamura ◽  
Kiyoaki Takiguchi ◽  
Akira Hirose ◽  
Hideki Hashimoto
Keyword(s):  
Electric Field ◽  
Human Body ◽  
Body Position ◽  
Position Estimation ◽  
Resonance Coupling ◽  
Estimation System
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