Dynamic Gesture Recognition Method Research Articles

Three-dimensional dynamic gesture recognition method based on convolutional neural network

With the rapid advancement of virtual reality, dynamic gesture recognition technology has become an indispensable and critical technique for users to achieve human–computer interaction in virtual environments. The recognition of dynamic gestures is a challenging task due to the high degree of freedom and the influence of individual differences and the change of gesture space. To solve the problem of low recognition accuracy of existing networks, an improved dynamic gesture recognition algorithm based on ResNeXt architecture is proposed. The algorithm employs three-dimensional convolution techniques to effectively capture the spatiotemporal features intrinsic to dynamic gestures. Additionally, to enhance the model’s focus and improve its accuracy in identifying dynamic gestures, a lightweight convolutional attention mechanism is introduced. This mechanism not only augments the model’s precision but also facilitates faster convergence during the training phase. In order to further optimize the performance of the model, a deep attention submodule is added to the convolutional attention mechanism module to strengthen the network’s capability in temporal feature extraction. Empirical evaluations on EgoGesture and NvGesture datasets show that the accuracy of the proposed model in dynamic gesture recognition reaches 95.03% and 86.21%, respectively. When operating in RGB mode, the accuracy reached 93.49% and 80.22%, respectively. These results underscore the effectiveness of the proposed algorithm in recognizing dynamic gestures with high accuracy, showcasing its potential for applications in advanced human–computer interaction systems.

Research on Dynamic Gesture Recognition and Control System based on Machine Vision

Hand gesture recognition and control is a new type of human-computer interaction that can provide a more convenient and efficient operation mode by utilizing non-contact gesture recognition technology. This paper presents a lightweight dynamic gesture recognition method for intelligent office presentation control. First, we introduce the concept of hand gesture recognition and go over key gesture recognition technologies like classification. The structure, process, and evaluation index of the gesture recognition algorithm are described in detail using a convolutional neural network model. During the experiment's algorithm verification phase, we test and analyze the algorithm using the Python language, compilation environment, and data set. In the control experiment, we evaluated the system's ability to control the office application's start, play, next, previous, and exit functions. We achieve 96.3% accuracy on the test set. Experimental results show that the system can recognize a wide range of hand gestures and accurately control the presentation.

Digital Media Design for Dynamic Gesture Interaction with Image Processing

With the rapid development of human-computer interaction technology and research in the fields of ergonomics and user experience, people have increasingly demanded robot availability and ease of use. The rapid development of modern computer computing and digital media technology has made people’s contact and interaction with computers more and more frequent, and they have appeared more in people’s daily lives. Therefore, the convenience and freedom of computer communication have been proposed. Based on the above background, the research content of this paper is the design of dynamic gesture interactive digital media based on image processing. This paper proposes a design scheme of an interactive touch system based on image processing and a dynamic gesture recognition method and uses the time difference method to experimentally simulate the system proposed in this paper. Image processing is the technique of analyzing images with a computer to achieve the desired results. Image processing technology generally includes three parts: image compression, enhancement and restoration, and matching, description, and recognition. The experimental results show that, for the same motion trajectory, the accuracy depends on the complexity of the gesture degree. And the system’s recognition accuracy rate is always maintained above 98%, confirming that the system has the performance requirements of high recognition accuracy, fast response speed, and stable work. Finally, the system was tested for performance and function, which verified that the system meets the real-time requirements. For a particular gesture operator, the accuracy rate has a great relationship with the standard degree of its operation. If the similarity with the model is high, you can achieve a high recognition rate.

Dynamic Gesture Recognition Method Based on Millimeter-wave Radar by One-Dimensional Series Neural Network

For the most of the existing gesture recognition methods based on the radar sensor, the parameters such as the distance, Doppler, and angle are estimated using the radar echo at first. And then the obtained data spectra are inputted into the convolutional neural networks to classify the gestures. The implementation process is complicated. A dynamic gesture recognition method is proposed based on the millimeter-wave radar using the One-Dimensional Series connection Neural Networks (1D-ScNN) in this paper. Firstly, the original echo of dynamic gesture is obtained by the millimeter-wave radar. The gesture features are extracted by the one-dimensional convolution and pooling operations, and then are inputted into the one-dimensional inception v3 structure. In order to aggregate the one-dimensional features, the Long Short-Term Memory (LSTM) modular is connected to the end of the network. The inter-frame correlation of dynamic gestures echo is fully utilized to improve the recognition accuracy and the convergence speed of training. The experimental results show that the proposed method is simple in implementation and has a fast convergence speed. The classification accuracy can reach more than 96.0%, which is higher than the traditional gesture classification methods.

Dynamic gesture recognition based on feature fusion network and variant ConvLSTM

Gesture is a natural form of human communication, and it is of great significance in human–computer interaction. In the dynamic gesture recognition method based on deep learning, the key is to obtain comprehensive gesture feature information. Aiming at the problem of inadequate extraction of spatiotemporal features or loss of feature information in current dynamic gesture recognition, a new gesture recognition architecture is proposed, which combines feature fusion network with variant convolutional long short-term memory (ConvLSTM). The architecture extracts spatiotemporal feature information from local, global and deep aspects, and combines feature fusion to alleviate the loss of feature information. Firstly, local spatiotemporal feature information is extracted from video sequence by 3D residual network based on channel feature fusion. Then the authors use the variant ConvLSTM to learn the global spatiotemporal information of dynamic gesture, and introduce the attention mechanism to change the gate structure of ConvLSTM. Finally, a multi-feature fusion depthwise separable network is used to learn higher-level features including depth feature information. The proposed approach obtains very competitive performance on the Jester dataset with the classification accuracies of 95.59%, achieving state-of-the-art performance with 99.65% accuracy on the SKIG (Sheffifield Kinect Gesture) dataset.

Gesture recognition and information recommendation based on machine learning and virtual reality in distance education

The dynamic and static gesture recognition in the distance education application scenario is not mature enough in theory at present, and still has a large space for development, and the application of gesture recognition in education is relatively insufficient. The purpose of this article is to combine gesture recognition with teacher classroom education and introduce a dynamic gesture recognition method. Moreover, this study introduces the data collection and preprocessing in detail and converts the data of the gesture action area into gray value images, and then uses the improved algorithm to perform classification. In addition, this study designs a control experiment to analyze the performance of the algorithm in this study and compares the accuracy of algorithm recognition from the perspective of simple background and complex background. The research results show that teaching gesture recognition in distance education can effectively improve education efficiency, with high accuracy, and can be directly applied to the system.

Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition

Gesture recognition has been applied in many fields as it is a natural human–computer communication method. However, recognition of dynamic gesture is still a challenging topic because of complex disturbance information and motion information. In this paper, we propose an effective dynamic gesture recognition method by fusing the prediction results of a two-dimensional (2D) motion representation convolution neural network (CNN) model and three-dimensional (3D) dense convolutional network (DenseNet) model. Firstly, to obtain a compact and discriminative gesture motion representation, the motion history image (MHI) and pseudo-coloring technique were employed to integrate the spatiotemporal motion sequences into a frame image, before being fed into a 2D CNN model for gesture classification. Next, the proposed 3D DenseNet model was used to extract spatiotemporal features directly from Red, Green, Blue (RGB) gesture videos. Finally, the prediction results of the proposed 2D and 3D deep models were blended together to boost recognition performance. The experimental results on two public datasets demonstrate the effectiveness of our proposed method.

Hardware Design of Codebook-based Moving Object Detecting Method for dynamic Gesture Recognition

Hardware Design of Codebook-based Moving Object Detecting Method for dynamic Gesture Recognition

Dynamic Gesture Recognition in the Internet of Things

Gesture recognition based on computer vision has gradually become a hot research direction in the field of human–computer interaction. The field of human–computer interaction is an important direction in the Internet of Things (IoTs) technology. Human–computer interaction through gestures is the direction of continuous research on IoTs technology. In recent years, the Kinect sensor-based gesture recognition method has been widely used in gesture recognition, because it can separate gestures from complex backgrounds and is less affected by illumination and can accurately track and locate gesture motions. At present, the Kinect sensor needs to be further improved on the recognition of complex gesture movements, especially the problem that the recognition rate of dynamic gestures is not high, which hinders the development of human–computer interaction under the IoTs technology. In this paper, based on the above problems, the Kinect-based gesture recognition is analyzed in detail, and a dynamic gesture recognition method based on HMM and D-S evidence theory is proposed. Based on the original HMM, the tangent angle and gesture change at different moments of the palm trajectory are used as the characteristics of the complex motion gesture, and the dimension of the trajectory tangent is reduced by the number of quantization codes. Then, the parameter model training of HMM is completed. Finally, combined with D-S evidence theory, combinatorial logic is judged, dynamic gesture recognition is carried out, and a better recognition effect is obtained, which lays a good foundation for human–computer interaction under the IoTs technology.

Dynamic Gesture Recognition based on LeapMotion and HMM-CART Model

This paper focuses on improving the recognition accuracy of dynamic gesture learning, and proposes dynamic gesture track recognition strategy based on HMM-CART model structure. It integrates the modeling ability of the HMM (Hidden-Markov-Model) for time series data and the interpretability of CART (Classification-And-Regression-Tree) for its ability of fast classification and regression, and use it for dynamic gesture recognition. Time series data of movement gestures collected by LeapMotion sensor will be first divided into four channels: finger shape, palm normal vector, palm ball radius and palm displacement vector, and then HMM in HMM Layer will be built for each channel, finally the likelihood probability of model calculated for each sub-sequence of observation should be classified as the input of the CART model in CART Layer to identify the gesture. Experiments show that the accuracy of dynamic gesture recognition method using HMM-CART model is higher than that of traditional single channel HMM.

Hand Gesture Recognition for Kinect v2 Sensor in the Near Distance Where Depth Data Are Not Provided

Kinect v2 sensor does not provide depth information and skeletal traction function in near distance from the sensor. That is why many researches, to recognize hand gestures, are focused on the skeletal tracking only inside the range of detection. This paper proposes a method which can recognize hand gestures in the distance less than 0.5 meter without conventional skeletal tracking when Kinect v2 sensor is used. The proposed method does not use the information of depth sensor and infrared sensor, but detect hand area and count the number of isolated areas which are generated by drawing a circle in the center of the hand area. This method introduces new detectable gestures without high cost, so that it can be a substitute for the existing mouse-movement controlling and dynamic gesture recognition method such as clicking a mouse, clicking and dragging, rotating an image with two hands, and scaling an image with two hands in near distance. The gestures are appropriate for the user interface of smart devices which employ the interactions based on hand gestures in near distance.

Real-time hand tracking using a mean shift embedded particle filter

Particle filtering and mean shift (MS) are two successful approaches to visual tracking. Both have their respective strengths and weaknesses. In this paper, we propose to integrate advantages of the two approaches for improved tracking. By incorporating the MS optimization into particle filtering to move particles to local peaks in the likelihood, the proposed mean shift embedded particle filter (MSEPF) improves the sampling efficiency considerably. Our work is conducted in the context of developing a hand control interface for a robotic wheelchair. We realize real-time hand tracking in dynamic environments of the wheelchair using MSEPF. Extensive experimental results demonstrate that MSEPF outperforms the MS tracker and the conventional particle filter in hand tracking. Our approach produces reliable tracking while effectively handling rapid motion and distraction with roughly 85% fewer particles. We also present a simple method for dynamic gesture recognition. The hand control interface based on the proposed algorithms works well in dynamic environments of the wheelchair.