The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the value is 0.92, with good comprehensive performance.

/pdf/dynamic-gesture-contour-feature-extraction-method-using-1igvjuwhvo.pdf

Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning

Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

/pdf/multifeature-fusion-human-motion-behavior-recognition-473aqafkt1.pdf

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

Sports can combine virtual reality technology with high-precision training to enhance the training effectiveness of users. In view of the current problems of insufficient physical quality and lack of exercise motivation faced by college students, this paper aims to explore a new type of college sports game teaching model through the integration of virtual reality technology. This is to improve students 'physical exercise efficiency and subjective initiative. This study constructed an experiential sports game model by applying posture recognition technology and fuzzy comprehensive evaluation method. The research indicated that the error accuracy with residual structure added will quickly decrease. When calculating the error, the overall error decreased by 6 mm. The proposed method can accurately distinguish human behavior in motion and trajectories in the motion area. The motion capture accuracy was high, and the deformation amount met the teaching needs of higher education institutions for sports game teaching. The contribution of this study is the significant improvement in the accuracy of motion capture, resulting in a total error reduction to 6 mm. This improvement ensured that the model can accurately distinguish between motion behavior and trajectory. The cumulative variance explanation rate of the scale reached 712.407 %, which was far more than that of the traditional model, and fully covered and explained the questionnaire information. Through the comparative test with four knowledge tracking models, the effectiveness and superiority of the sports game teaching mode integrated with virtual reality technology were verified. 

Sports game teaching and high precision sports training system based on virtual reality technology

Due to the influence of environmental interference and too fast speed, there are some problems in ski motion capture, such as inaccurate motion capture, motion delay, and motion loss, resulting in the inconsistency between the actual motion of later athletes and the motion of virtual characters. To solve the above problems, a real-time skiing motion capture method of snowboarders based on a 3D vision sensor is proposed. This method combines the Time of Fight (TOF) camera and high-speed vision sensor to form a motion acquisition system. The collected motion images are fused to form a complete motion image, and the pose is solved. The pose data is bound with the constructed virtual character model to drive the virtual model to synchronously complete the snowboarding motion and realize the real-time capture of skiing motion. The results show that the motion accuracy of the system is as high as 98.6%, which improves the capture effect, and the motion matching proportion is better and more practical. It is also excellent in the investigation of motion delay and motion loss.

/pdf/real-time-capture-of-snowboarder-s-skiing-motion-using-a-3d-4at4kemrzu.pdf

Real-Time Capture of Snowboarder’s Skiing Motion Using a 3D Vision Sensor

Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years.Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods.Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up.Deep Neural Networks (DNNs) and their derived models are integral to modern artificial intelligence recognition methods.In addition, technologies that were widely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves, data sensors (such as Kinect, Leap Motion, etc.), and high-definition photography.Meanwhile, facial expression recognition, complex background processing, and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language, accuracy, robustness, real-time performance, and user independence are significant challenges for future sign language recognition research.Additionally, suitable datasets and evaluation criteria are also worth pursuing. 

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.

/pdf/gesture-recognition-algorithm-of-human-motion-target-based-1ikbbgd1cf.pdf

Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network

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Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network