73 results about "Gesture classification" patented technology

Systems and methods for content processing can include obtaining a gesture input and display data, determining content selected by the gesture input, classifying the gesture, and performing a particular data processing action based on the content selection and the gesture classification. The particular data processing action can vary based on gesture classification. The content selection determination can include determining a gesture mask and then determining the features of the displayed content item that are within the gesture mask.

The invention discloses a gesture recognition method and device and electronic equipment, and belongs to the technical field of gesture recognition. The method comprises the following steps: carrying out hand detection on a picture containing a hand image by adopting a pre-trained hand detection model, and obtaining position information of a hand detection frame corresponding to the picture; determining a hand region in the picture based on the position information of the hand detection frame; adopting a pre-trained hand key point detection model to detect hand key points contained in the picture of the hand region, and obtaining position information of the hand key points; and performing gesture recognition processing on the position information of the hand key points by adopting a pre-trained gesture recognition model to obtain a gesture recognition result matched with the hand image. According to the method, the consecutive hand detection, hand key point detection and gesture classification processes are adopted, so that the accuracy of hand key point detection is improved, the accuracy of gesture recognition is improved, and the robustness of gesture recognition in a complex environment can be improved.

The invention relates to the technical field of computer vision and human-computer interaction, and discloses a self-adaptive hand image recognition method and system based on multi-source feature fusion. The method comprises the following steps: acquiring a real-time video stream image from a camera, carrying out various filtering processing on the real-time video stream image, and respectively generating an illumination enhanced image, a background subtraction mask, a skin color area mask and a dynamic motion mask; dynamically adjusting the fusion weight of each filtering processing result according to the scene features of the real-time video stream image, and generating comprehensive filtering output through weighted fusion; performing multi-stage hand segmentation processing on the comprehensive filtering output to obtain an initial hand region mask, performing time sequence smoothing processing, and extracting a maximum connected domain as a final hand region mask; and inputting the final hand region mask into a convolutional neural network for gesture recognition, outputting a prediction probability of each type of gestures, and displaying a gesture classification result in real time. According to the method, the adaptability of hand detection to strong interference backgrounds and variable environments is enhanced.

Systems and methods for content processing can include obtaining a gesture input and display data, determining content selected by the gesture input, classifying the gesture, and performing a particular data processing action based on the content selection and the gesture classification. The particular data processing action can vary based on gesture classification. The content selection determination can include determining a gesture mask and then determining the features of the displayed content item that are within the gesture mask.

The invention discloses a gesture recognition method based on entropy features and an improved PSO-SVM. The gesture recognition method comprises the following steps that S1, surface electromyogram signals of multiple gestures are collected and preprocessed; s2, performing multi-scale decomposition on the surface electromyogram signals by adopting a variational mode decomposition method, and extracting entropy features; s3, performing dimension reduction processing on the entropy features by using correlation analysis, a random forest and a Lasso method to obtain dimension-reduced feature data; and S4, classifying the dimension reduction feature data by adopting a gesture classification model to realize gesture recognition. According to the method, the entropy features of the surface electromyogram signals are extracted through the variational mode decomposition method, the parameters of the support vector machine are optimized in combination with the particle swarm optimization algorithm, the gesture classification model is constructed, the influence of a traditional time domain and frequency domain feature extraction mode on the robustness of the gesture classification model is effectively solved, and the classification precision and robustness are improved.

The invention provides a dynamic gesture recognition method and device, electronic equipment, a chip and a medium, and relates to the technical field of man-machine interaction. The method comprises the following steps: carrying out key point identification on an image in a video frame collected by a binocular camera, and respectively determining a first three-dimensional key point and a second three-dimensional key point; identifying the gesture category of the target gesture in the video frame as a first gesture category by using a dynamic gesture classification model; based on the corresponding relation between the reference waveform feature and the gesture category, using a signal recognition algorithm to analyze the waveform feature formed by the first stereo key point and / or the second stereo key point, and recognizing a second gesture category corresponding to the target gesture in the video frame; and checking whether the first gesture category is consistent with the second gesture category to determine a target gesture category. Through the technical scheme provided by the invention, the problems of low accuracy and low stability of dynamic gesture recognition are solved, the accuracy and the stability of gesture recognition are improved, and the user experience of gesture interaction is improved.

Systems and methods for content processing can include obtaining a gesture input and display data, determining content selected by the gesture input, classifying the gesture, and performing a particular data processing action based on the content selection and the gesture classification. The particular data processing action can vary based on gesture classification. The content selection determination can include determining a gesture mask and then determining the features of the displayed content item that are within the gesture mask.

An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Methods and apparatuses (devices, methods, etc.) for determining gestures, e.g., hand gestures, from a wrist and / or forearm worn array of magnetometers. These methods and apparatuses may use a trained machine-learning agent to identify gestures from magnetic signals that have been preprocessed to optimize gesture detection. The trained machine learning agent may apply a hierarchical classification that first identifies a first category of gestures from a second category of gestures and then classifies between the individual gestures within each category. The methods and apparatuses described herein may be of particular use with acoustically driven ferromagnetic resonance (ADFMR) sensors.

The invention relates to the technical field of virtual classrooms, and discloses a real-time collaborative virtual classroom interaction method, which comprises the following steps of: constructing a classroom gesture operation comparison table according to preset virtual classroom gestures; capturing an infrared image of a hand of a user and joint point coordinate data in real time, and performing preprocessing; performing feature extraction on the preprocessed infrared image to realize gesture classification to obtain gesture description; searching in a classroom gesture operation comparison table according to the gesture description to determine whether gesture motion judgment is started or not, and determining a virtual object corresponding to the gesture; detecting an operation request user of the same virtual object in the same time interval, and executing a preset conflict execution mechanism; and executing the corresponding virtual operation result so as to map the virtual operation result into virtual classroom operation. According to the method, the gesture comparison table is constructed, data collection and recognition and multi-user conflict processing are optimized, accurate mapping from gestures to virtual operation is achieved, interaction accuracy and cooperation fluency are improved, and immersive experience of a virtual classroom is enhanced.

The invention provides a gesture recognition method based on an optical fiber sensing technology and deep learning, and relates to the technical field of optical fiber sensing. According to the gesture recognition method, gesture information is captured through speckle image changes of the multimode optical fiber fixed on a glove under different gestures by utilizing the deformation characteristic of the multimode optical fiber, and high-precision gesture classification is carried out in combination with a convolutional neural network (CNN) and a self-attention mechanism. The specific implementation of the method comprises the steps of layout and fixation of multimode optical fibers, speckle image acquisition, data preprocessing, neural network model design and training and the like. Wherein a 635nm semiconductor laser is used as a light source, a CCD camera is used for collecting speckle images in real time, a Python script is used for carrying out optimization processing and data enhancement on the images, a final deep learning model is composed of a feature extraction layer, a global feature modeling layer and a classification layer, and high-precision recognition of gestures is achieved.

The embodiment of the invention discloses a gesture recognition method and electronic equipment. The method comprises the following steps: performing image acquisition on a user gesture through an image acquisition device to obtain a continuous video sequence; performing gesture region extraction on each image frame in the continuous video sequence to obtain a gesture image, and performing feature extraction on the gesture image to obtain spatial stream features; performing optical flow calculation on two continuous frames of images to obtain optical flow images, and performing feature extraction on the optical flow images to obtain time flow features; and fusing the spatial flow features and the time flow features to obtain fused features, and performing gesture classification on the fused features based on a classifier to determine a gesture recognition result. According to the scheme, the space features and the time features can be subjected to cross-modal correlation fusion, so that the two features are complementary in gesture recognition, the motion features can know that the apparent features pay attention to a dynamic area, the border features can correct misjudgment of the operation features, and the accuracy of gesture recognition is improved.

The invention relates to the technical field of electromyographic signal processing, in particular to an electromyographic acquisition signal processing method and system for gesture classification. The method comprises the following steps: forming a signal label and an electromyographic signal sample cluster based on a gesture type, electromyographic signal acquisition duration and gesture recognition accuracy; calibrating a cross-cluster error value of the electromyographic signal features; mapping the cross-cluster error value to a two-dimensional coordinate system and adding a point feature value; and generating a to-be-processed sample set through the dual-scale data screening window, judging a gesture type membership degree, and executing visual display. According to the method, the classification precision problem caused by user action habit differences is solved through intelligent data analysis, the accuracy and adaptability of gesture classification are improved, and the method is suitable for scenes such as human-computer interaction and rehabilitation assistance.

The present invention proposes a two-stream cyclic attention dynamic gesture recognition method based on motion guidance. The method includes: using a video codec tool to extract motion vectors, and combining a prediction frame residual analysis mechanism to quantify the dynamic intensity of the hand and generate a motion mask; using a gated network and a graph neural network for the RGB features and the hand motion contour mask to perform gated fusion and graph convolution calculations in sequence; using a dynamic spatio-temporal decay module and a multi-scale wavelet spectrum-spatial block to perform dilated convolution processing, as well as multi-scale frequency domain reconstruction and channel interaction respectively to obtain a gesture classification result. The present invention proposes a cyclic attention mechanism based on a fully convolutional architecture, combined with a learnable time decay strategy, to achieve efficient and low-complexity temporal modeling. At the same time, a wavelet decomposition and reconstruction and multi-scale pooling method are adopted to capture fine-grained hand motion changes, enabling the network to effectively model short-term temporal dependence features and further improving the recognition accuracy of dynamic gestures.

The application belongs to the field of millimeter wave radar perception and human posture recognition, and discloses a non-contact personnel identity and posture recognition method based on motion instability compensation, comprising the following steps: step 1, static background points are adaptively screened, the radar self-motion speed is estimated, and the point cloud coordinates and the micro-Doppler spectrum are compensated; step 2, a GesAuthNet double-branch deep neural network is built to obtain shape parameters and posture parameters; step 3, an identity authenticator is constructed in combination with the shape parameters to authenticate the legitimacy of the user, and a gesture classifier is constructed in combination with the posture parameters to identify the gesture of the user. The application has the advantages of motion instability compensation capability, robust gesture feature extraction capability, high-precision identity authentication and gesture recognition, and strong stability and robustness.

The application provides a gesture recognition method based on a head-mounted display device, comprising: inputting an image into a hand key point detection model, the hand key point detection model comprising a plurality of stacked feature extraction modules, sequentially adopting the plurality of stacked feature extraction modules to perform feature extraction on the input image to obtain a feature map of a preset size; expanding the feature map of the preset size into a one-dimensional vector through a dimension expansion layer, and adopting a fully connected layer to perform regression prediction of hand key points to obtain coordinates of the hand key points; and obtaining a gesture classification result in the image through a combination relationship between the coordinates of the hand key points. The application combines a hand detection model and a hand key point detection model to realize detection of a gesture, since the network structures of the hand detection model and the hand key point detection model are substantially the same, and only differ in a task head, the full network structure of the gesture detection of the application has small calculation amount and low power consumption under the condition of meeting detection accuracy.

The present invention discloses a wearable gesture interaction system and method based on multimodal flexible sensing. The wearable gesture interaction system includes: two modal flexible sensors, namely a flexible strain sensor and a flexible acceleration sensor, for capturing gesture simulation signals indicating finger bending state, three-dimensional hand posture, and hand swiping; a data acquisition module for converting the gesture simulation signals into digital gesture signals and outputting them in real time via a wireless communication module; and a host computer for receiving the digital gesture signals, performing gesture activity segmentation and preprocessing to obtain a Grammar angle field map of the gesture signals. A deep learning model based on a self-attention mechanism is then used to extract and fuse multimodal features to achieve gesture classification for gesture human-computer interaction. The present invention uses multimodal flexible sensors combined with deep learning to improve the accuracy of gesture recognition. Furthermore, the system is comfortable to wear and relatively low in cost, effectively meeting user requirements.

The invention discloses a multi-finger gesture recognition method and system based on frequency modulation continuous waves and an ultrasonic transducer, and belongs to the technical field of man-machine interaction. According to the method, gesture reflection echo signals are collected through a pMUT array, and a dual-channel time-distance Doppler sequence feature map and a time-dual-channel phase difference sequence feature map are obtained through processing such as noise suppression, feature extraction and feature enhancement; and inputting the fused three-channel feature map sequence into a double-flow collaborative attention mechanism-three-dimensional convolutional neural network-long and short-term memory network neural network model to realize multi-finger micro-gesture classification and recognition, and realizing full-process automation based on threshold triggering. On the basis, the invention further provides a pMUT multi-finger micro gesture recognition system. The method has the advantages of being low in operation power consumption, high in environment adaptability, high in resolution precision and the like, and is suitable for the fields of virtual reality, vehicle-mounted control, mechanical arm control and the like.

The invention discloses a hand shape recovery model training method, a hand shape recovery method, an electronic device and a medium, and relates to the technical field of artificial intelligence, the method comprises the steps that a pre-constructed training data set is acquired, and the training data set comprises a category annotation data set, an unannotated data set and a posture annotation data set; performing supervision pre-training on a preset gesture classification model based on the category labeling data set to obtain a pre-trained gesture classification model; constructing a gesture feature model based on the pre-trained gesture classification model, and performing unsupervised pre-training on the gesture feature model based on the unlabeled data set to obtain a pre-trained gesture feature model; and constructing a hand shape recovery model based on the pre-trained gesture feature model, and performing parameter fine tuning training on the hand shape recovery model based on the gesture labeling data set to obtain a trained hand shape recovery model. According to the method, the dependence of hand shape recovery model training on the labeled data is reduced, so that the training cost of the model is reduced.

This invention discloses a five-finger dexterity hand grasping detection method based on soft-mask region representation and multi-task learning. This method decomposes high-dimensional continuous grasping parameters into three sub-tasks: grasping quality prediction, grasping width regression, and grasping gesture classification. It constructs a soft-mask multi-color grasping region representation to generate pixel-level grasping quality, width, and gesture labels. A grasping-oriented channel-space-geometric attention mechanism is designed to construct a lightweight multi-task generative grasping detection network. Taking RGB-D images as input, it outputs grasping quality maps, width maps, and gesture maps in parallel. An adaptive weighted loss function based on effective region constraints is used for training to suppress background interference and dynamically balance multi-task learning. During inference, the grasping center is located by searching for peaks in the quality map, and the corresponding width and gesture are read to achieve single-target or multi-target grasping detection. This invention improves the accuracy, real-time performance, and robustness of grasping detection in multi-object scenes while reducing model complexity.

The invention provides a multi-channel ultrathin flexible stretchable myoelectricity sensor and a preparation method thereof, and relates to the technical field of biological electrodes, the sensor comprises a flexible substrate, a processor and a plurality of sensing units; the sensing units are uniformly arranged on the flexible substrate; each sensing unit is used for acquiring a plurality of electromyographic signals and a plurality of muscle impedance signals within a set time period; the processor is used for performing feature extraction on each electromyographic signal to obtain a plurality of time domain features and a plurality of frequency domain features; the processor is used for performing feature extraction on each muscle impedance signal to obtain a plurality of impedance amplitude features; carrying out splicing fusion to obtain a fusion feature vector; and the processor obtains gesture classification based on the fused feature vector in combination with the classifier. According to the method, the problems of low recognition rate and poor robustness caused by unstable signals, single information dimension and signal asynchronization are solved.

Embodiments of the present application provide a gesture classification method and related equipment, and relate to the field of intelligent control; in the method, hand three-dimensional key points are obtained by using a hand two-dimensional image, the cost of obtaining hand depth information can be reduced without increasing hardware cost; first gesture classification information is determined based on the hand two-dimensional image, and second gesture classification information is determined based on the hand three-dimensional key points; third gesture classification information of the hand two-dimensional image is determined based on the first gesture classification information and the second gesture classification information, the use of rich texture information of the two-dimensional image and spatial information of the three-dimensional key points can be compatible with plane gestures and three-dimensional gestures, and the accuracy of gesture classification can be effectively improved.

The application belongs to the field of biomedical signal processing and human-computer interaction, and discloses a muscle-derived signal gesture recognition method and system based on crown porcupine optimization VMD parameters. The method collects electromyography or muscle magnetic signals during gesture action, and constructs a sample set through sliding window segmentation, data balancing processing and standardization. The crown porcupine optimization algorithm (CPO) is used to adaptively optimize the mode number and penalty factor of variational mode decomposition (VMD), and the gesture classification accuracy is used as the fitness function. Global search and local mining are realized through the four-layer defense of vision, sound, odor and physics of CPO. The intrinsic mode function (IMF) under the optimal parameters is extracted and spliced into a multi-dimensional feature matrix, which is input into a classification model to realize gesture recognition. The application directly optimizes the recognition performance, enhances the feature discriminability, and improves the recognition accuracy and robustness of muscle-derived signals in complex scenes.

The embodiment of the invention provides a multi-mapping processing method for myoelectricity gesture classification, which belongs to the technical field of data recognition and specifically comprises the following steps: step 1, processing original data of myoelectricity signals by using a moving average power method to obtain myoelectricity data; step 2, performing logarithmic function mapping on the myoelectricity data; step 3, linearly scaling the data after logarithmic function mapping processing into a preset interval; step 4, performing activation function operation on the linearly scaled data; step 5, making the data after the activation function operation into a grey-scale map; and step 6, training a Resnet50 model by using the grey-scale map to obtain a classification model to classify the electromyographic signals to be classified, and obtaining a gesture classification result. Through the scheme of the invention, the classification efficiency, accuracy and adaptability are improved.

The invention discloses a lightweight attitude classification method fusing human body key points and target detection, and relates to the technical field of image processing. The method comprises the following steps: acquiring target image data, and extracting pixel point data of one frame of image in the target image data through a computer vision library; pixel point data of the obtained image is input into a posture estimation model, and the posture estimation model processes the pixel point data and returns the recognized human body key points and the target detection result; performing attitude classification according to a detection result, wherein the attitude can be divided into a static type and a dynamic type; and outputting a final posture form by combining the classification of the static postures and the classification of the dynamic postures. According to the method, a relatively simple machine learning classifier is adopted, and the character postures in the video are classified based on the human body key points and the target detection result which are almost obtained in real time, so that compared with the prior art, the time delay is lower, and the required hardware resources are fewer.

An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

The present invention relates to a three-dimensional dynamic gesture recognition method based on an improved dynamic time warping algorithm, belonging to the field of robot vision. The hardware system of the method includes a left camera, a right camera and a host computer that are parallel to each other; the specific recognition operation steps are as follows: (1) static gesture recognition, selecting the static gesture "one" of extending one finger as the recognition stop instruction, and the static gesture "five" of extending five fingers as the recognition start instruction; (2) eliminating redundant movements; (3) obtaining the three-dimensional motion trajectory sequence of the right wrist node and the three-dimensional motion trajectory sequence of the right elbow node; (4) completing the three-dimensional dynamic gesture recognition based on the established three-dimensional dynamic gesture classification template library. The three-dimensional dynamic gesture recognition method of the present invention enables the three-dimensional dynamic gesture recognition system to have a higher recognition accuracy rate for complex three-dimensional dynamic gestures.

The invention discloses a gesture recognition method and device and a gesture control method and device. The gesture recognition method comprises the following steps: acquiring object gesture image data; performing fingertip detection on the gesture image data to obtain a fingertip recognition feature vector; performing feature extraction on the gesture image data based on a gesture classification model to obtain a gesture recognition feature vector; and fusing the fingertip recognition feature vector and the gesture recognition feature vector in a decision-making layer, and performing analysis processing to obtain a gesture recognition result. The gesture recognition accuracy can be improved.