126 results about "Hand region" patented technology

The invention relates to the technical field of behavior recognition, in particular to a machine learning-based surgical personnel video behavior recognition method and system. The method comprises the following steps: acquiring a video frame sequence and a point cloud frame sequence from a hand washing area of an operator through an RGB camera and laser ranging equipment to obtain the video frame sequence and the point cloud frame sequence; performing hand point cloud segmentation on the point cloud frame sequence to obtain hand point cloud data; performing action original data generation according to the video frame sequence and the hand point cloud data to obtain action original data; and extracting position and attitude information of hand key points from the original motion data to obtain video key point feature data. Visual information is captured through the RGB camera, three-dimensional depth information is obtained through the laser ranging device, fine analysis and evaluation are carried out on actions in combination with the deep learning network, and finally higher recognition precision, robustness and automation level are achieved.

The invention belongs to the technical field of human-computer interaction, and discloses a digital twin WEB application air gesture interaction method and system. Comprising the following steps: collecting a real-time video stream of a camera of user side equipment, and preprocessing the real-time video stream; x hand region frames are obtained; inputting the X hand region frames into a preset hand key point detection model based on a generative adversarial network to obtain hand key point coordinate data; acquiring a hand skeleton model based on the hand key point coordinate data; acquiring a joint angle and a hand motion track according to the hand skeleton model; mapping the hand skeleton model into a digital twin virtual scene, presetting n gesture instruction modes in the digital twin virtual scene, and matching the hand skeleton model, the joint angle and the hand motion track with the gesture instruction modes; personalized and extensible gesture interaction capability is provided, and man-machine interaction modes are enriched.

Example systems, devices, media, and methods are described for controlling the presentation of one or more virtual or graphical elements on a display in response to bimanual hand gestures detected by an eyewear device that is capturing frames of video data with its camera system. An image processing system detects a first hand and defines a mapped region relative to a surface of the detected first hand. The system presents a virtual target icon at a position within or near the mapped region. The image processing system detects a series of bimanual hand shapes, including the detected first hand and at least one fingertip of a second hand. In response to determining whether the detected series of bimanual hand shapes matches a predefined hand gesture, the system executes a selecting action that includes presenting one or more graphical elements on the display.

The invention belongs to the field of millimeter-wave radar sensing and hand posture estimation, and discloses a millimeter-wave radar-based multi-target hand posture estimation method, which comprises the following steps of: 1, creating a hand region: detecting all objects in a millimeter-wave radar sensing region, detecting a predefined wake-up gesture through a wake-up detection model to recognize a target user, the method comprises the following steps: step 1, evaluating the occurrence probability of a wake-up gesture, and recognizing a hand region, step 2, in the created hand region, constructing a distance-azimuth-pitching three-dimensional feature cube which represents hand posture features; and step 3, estimating hand posture features through an m < 2 > HandNet model. The millimeter wave radar is used as a research object, hand posture estimation is achieved through the millimeter wave radar, multi-user and multi-hand interaction is supported, the shielding problem is effectively solved, and wide application scenes such as intelligent home control, intelligent terminal interaction, virtual reality equipment and sign language recognition are met.

A fighting glove includes a first padding section and a second padding section disposed in a dorsal side of a user's hand, and a strapping section configured to secure the glove to the user's hand. The first padding section includes multiple impact absorption layers, multiple slip layers, and a contour layer, which are each partially bonded to each other. In particular, the hand regions of the layers are securely attached to each other, and the hand body regions of the layers are not attached to each other such that the hand body regions of the layers are free to move independently from each other. Further, the fighting glove includes an exterior layer formed by a single fabric material in each of the inner side and the outer side of the exterior layer to mitigate the glove from forming protrusions or rough features on the exterior layer of the glove.

A pressing tool includes a housing and pressing jaws arranged outside the housing. One or both pressing jaws can be pivoted into the open position by a user's hand acting upon the respective pressing jaw that faces the housing. An actuating switch is formed on a pin-like holding projection having a longitudinal extent with a free end which protrudes from the housing at an acute or right angle. A dimension of the holding projection transverse to the longitudinal extent is smaller than a smallest diameter of the hand region.

The invention discloses a bimodal gesture recognition method and device in a complex scene, and the method comprises the steps: synchronously collecting an RGB image sequence and a plurality of event frames, obtaining a hand region prediction frame corresponding to each event frame based on the collected RGB image sequence and the plurality of event frames, a preset target detection network, and a preset hand frame prediction model, the hand region prediction frame comprises a first type of hand region prediction frame and a second type of hand region prediction frame; and inputting the synchronously acquired RGB image sequence, the plurality of event frames and the corresponding hand region prediction frame into the fusion skeleton point extraction network to obtain gesture skeleton point information of each frame, and feeding back and inputting the gesture skeleton point information of each frame into the hand region prediction model. According to the bimodal gesture recognition method and device in the complex scene, real-time recognition is carried out on the scene where dynamic and static gestures appear in a mixed mode, and the recognition accuracy is guaranteed.

The invention relates to the technical field of visual inspection, and particularly provides a method for detecting a hand joint in glasses equipment, a storage medium, electronic equipment and a product, and the method can comprise the steps: obtaining the confidence coefficient of a hand region corresponding to a current frame image; under the condition that the confidence coefficient is determined to be greater than a preset threshold value, processing the hand region to obtain a to-be-detected hand region image; and inputting the to-be-detected hand region image into a target hand joint detection model to obtain a hand joint detection result. According to some embodiments of the invention, the accuracy of hand joint detection can be improved.

Electrically heated motorcycle glove with temperature overload control and uniform heating, characterized in that it comprises a back of the hand region (1), a palm region (2), a lacing (3) and a wrist region (4), wherein the back of the hand region (1) has a back of the hand base surface (11) in the interior of which a heating layer (14) is installed, in the central region of which an adjusting element (15) is installed, wherein the palm region (2) has a palm base surface (21) in the interior of which a heating layer (14) is installed, wherein the wrist region (4) has an extended thermal protection layer (41), above which an operating switch (42) is attached, and in the lower region of the extended thermal protection layer (41) a battery compartment (45) is formed, in the interior of which a rechargeable battery (46) is accommodated;wherein the back of the hand base (11) and the palm base (21) are sewn with sewing thread, the back of the hand base (11) and the extended heat protection layer (41) are sewn with sewing thread, and the palm base (21) and the extended heat protection layer (41) are sewn with sewing thread;

The invention relates to the technical field of part assembly detection, in particular to a visual identification method and system for a part assembly process, and the method comprises the following steps: obtaining a video stream in real time, carrying out the decoding preprocessing, and generating continuous frame images; identifying a hand region and an operation button region in the frame image, and judging the process starting condition by calculating the intersection-union ratio of the hand region and the operation button region; performing part state acquisition on the frame image based on the classification model, identifying the part state through a multi-frame joint judgment method, comparing the part state with a preset part target state, and judging a process expected condition of each part state; when the process meets the expectation, a target detection algorithm is combined with a multi-frame joint technology to carry out remnant identification; and when the expected condition of the working procedure is not consistent or the remnants are identified, outputting an alarm. The method has the advantages of being low in use cost and high in universality and adaptability.

The invention relates to the technical field of data processing, and provides a gesture detection method and device, intelligent glasses, a system and a storage medium, and the method comprises the steps: obtaining a target image obtained by photographing a hand of a user through the intelligent glasses; receiving a detection signal transmitted by a signal transmitting device of a user; determining the position of the signal transmitting equipment relative to the intelligent glasses according to the receiving time of the detection signal; determining the position of a hand region in the target image according to the position of the signal transmitting equipment relative to the intelligent glasses; and performing gesture detection on the target image according to the position of the hand region in the target image so as to reduce the omission ratio and the false detection ratio of the hand of the user in the target image, improve the positioning accuracy of the hand of the user in the target image and improve the gesture detection effect of the target image.

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.

The invention relates to a parameter determination method and device of a low-voltage insulating glove, computer equipment, a readable storage medium and a program product. The method comprises the steps of obtaining operation parameters according to operation behavior parameters and operation environment parameters of a target operation scene; inputting the operation parameters into a trained friction analysis model to obtain friction analysis results corresponding to the hand areas in the target operation scene output by the friction analysis model; according to the friction analysis result of each hand region, glove reinforcing region parameters corresponding to the target operation scene are obtained; counting hand size data of each operator in the target operation scene, and obtaining glove size category parameters according to data distribution of the hand size data; and obtaining a target parameter set of the low-voltage insulating gloves applied to the target operation scene according to the glove reinforcing area parameters and the glove size category parameters. By adopting the method, the parameters of the low-voltage insulating gloves, which are more targeted to the target operation scene, can be obtained.

The invention provides a gesture interaction method and device, electronic equipment, a chip and a medium, is applied to an in-vehicle infotainment system, and relates to the field of man-machine interaction. The gesture interaction method comprises the following steps: acquiring a hand region image; detecting hand key points through a hand key point detector based on the hand region image; based on the hand key point, determining a finger pointing category through a finger pointing classifier; and controlling the vehicle to execute a first function corresponding to the finger pointing type. Through the technical scheme provided by the invention, the problems of low finger pointing recognition accuracy and low recall rate in related technologies are solved, the finger pointing recognition accuracy is improved, and a user obtains better man-machine interaction experience in a vehicle cabin.

The invention discloses an XR gesture tracking and recognition method based on multi-view depth fusion. The method comprises the following steps: constructing a virtual camera by adopting a perspective cutting method, and dynamically extracting a hand region after geometric correction through gesture detection and perspective cutting of each frame of image; in combination with internal and external parameters of a camera, through position coding and a multi-head attention mechanism, a geometrical relationship between visual angles is modeled, semantic relevance of multi-visual-angle 3D features is deeply excavated, globally consistent hand representation is constructed, then six-degree-of-freedom information of wrist joints and rotation angles of the joints are output based on a regression device, and forward kinematics and a linear skin algorithm are combined, so that the degree of freedom of the wrist joints is calculated. Generating a complete hand posture and skeleton key point coordinates; and based on the key point position and the rotation angle of each joint, gesture recognition is realized through template matching. According to the method, multi-view image information and hand kinematics constraints are combined, high-precision and high-robustness three-dimensional gesture attitude estimation and classification are achieved while real-time performance is guaranteed, and the method is suitable for various XR scenes.

A method for monitoring occupants of a vehicle comprises identifying a respective body region for one or more occupants of the vehicle within an obtained image; identifying within the body regions, skeletal points including points on an arm of a body; identifying one or more hand regions; and determining a hand region to be either a left or a right hand of an occupant in accordance with its spatial relationship with identified skeletal points of the body region of an occupant. The left or right hand region for the occupant are provided to a pair of classifiers to provide an activity classification for the occupant, a first classifier being trained with images of hands of occupants in states where objects involved are not visible and a second classifier being trained with images of occupants in the states where the objects are visible in at least one hand region.

The application relates to the technical field of image three-dimensional reconstruction. The application discloses a monocular hand-object interaction three-dimensional reconstruction method and device, a terminal and a storage medium, which can improve the quality of a hand-object interaction reconstruction result. The method comprises the following steps: acquiring an initial object grid, an initial hand grid and a target hand-object interaction grid corresponding to a target image; performing hand region and object region extraction processing on the target hand-object interaction grid to obtain a target hand rough region and a target object rough region; performing coarse registration of the initial object grid to the target object rough region by using an object registration algorithm to obtain an optimized object grid; performing coarse registration of the initial hand grid to the target hand rough region by using a hand registration algorithm to obtain an optimized hand grid; and performing parameter adjustment processing on the optimized object grid and the optimized hand grid by using a joint optimization algorithm to obtain a hand-object interaction reconstruction result corresponding to the target image.

The invention provides a finger motion capturing method, system and device, a storage medium and a program product, and the method comprises the steps: obtaining a target person image containing a target hand; detecting a target position of a target hand in the target person image; cutting a hand region image of a target hand in the target person image based on the target position; acquiring hand model parameters based on the hand region image; and acquiring a hand model of the target hand based on the hand model parameters. According to the scheme, the hand region image can be extracted from the target person image in a large visual angle range, and then the hand model parameters are acquired by utilizing the hand region image, so that the finger motion capturing method can keep robustness and stability in a large-range field, and the adaptability of the finger motion capturing method is improved.

The invention provides a dynamic gesture preprocessing and authentication method, a medium and equipment. The method comprises a preprocessing stage and an authentication stage. The preprocessing stage comprises the following steps: carrying out hand region segmentation and skeleton information extraction on input video data; performing standardization processing on the input video data to obtain a standardized image sequence and a corresponding skeleton sequence; the authentication stage comprises the steps of inputting a standardized image sequence and a corresponding skeleton sequence into an appearance and motion network for feature extraction; the appearance and motion network comprises appearance branches and motion branches; the appearance branch extracts appearance features from the standardized image sequence, and the motion branch extracts motion features from the skeleton sequence; and carrying out feature level fusion through a self-adaptive recoupling and fusion mechanism to obtain a final identity feature. According to the method, a double-flow authentication network is adopted, decoupling of appearance and motion features is achieved, targeted extraction and complementary expression of the features are achieved, and therefore the authentication efficiency and accuracy are improved.

The invention discloses a binocular camera for quickly calculating hand depth and electronic equipment, and the camera comprises a first receiver which is used for receiving a first signal of a palm and generating a first image; the second receiver is used for receiving a second signal of the palm and generating a second image; and the processor is used for detecting a first hand region on the first image, reducing the first hand region to a preset size, reducing the second image at the same magnification as the first hand region, estimating a second hand region on the second image, and then carrying out depth calculation. According to the method, the calculation amount of search and reconstruction is greatly reduced, a special Asic chip or special hardware is not needed, the depth of the hand is calculated in real time, and help is provided for subsequent gesture recognition.

The invention discloses a finger joint point spatial position estimation method, a finger joint point spatial position estimation device and head-mounted display equipment. The method comprises the following steps: performing hand recognition on an image acquired by a depth camera through a palm region detection model, and if the image has a hand region, marking the hand region from the image to obtain a hand region image; correcting the hand region image to obtain a distortionless hand region image; identifying the pixel coordinates of the articulation points in the distortionless hand region image and the relative distance of each articulation point through an articulation point detection model; and according to the pixel coordinates of the joint points and the relative distance, calculating through a preset objective function to obtain the absolute distance of the joint points. According to the invention, the calculation amount in the 3D hand joint point estimation process can be reduced, and the technical effects of meeting the high real-time requirement in AR / VR application are achieved.

The invention provides a hand data set generation method and electronic equipment. The hand data set generation method is used for improving the quality of a hand data set. Comprising the following steps: based on 2D projection position coordinates of each hand key point in a real image of a hand and a mask image of each hand region, screening mask images of each hand region obtained based on real image segmentation to obtain a mask image of a target hand region; identifying a hand boundary line of the target hand region by using the mask image of the target hand region to obtain a mask image of the hand boundary line; for any background image, obtaining an initial hand composite image according to the background image and a hand region image obtained by segmenting the real image by using the mask image of the target hand region; obtaining a mask image of boundary noisy points in the initial hand composite image through the mask image of the hand boundary line; and obtaining a target hand data set based on a target hand composite image obtained by smoothing the boundary noise points in the initial hand composite image according to the mask image of the boundary noise points.

The invention discloses a gesture recognition method and device based on a pulse camera, a medium and a product, relates to the technical field of gesture recognition, and discloses the gesture recognition method based on the pulse camera, and the method comprises the steps: taking pulse image data collected by the pulse camera as the input of a hand detection network, and obtaining dimension rising feature data based on an expansion convolution layer; expanding the dimension raising feature data in an inverse residual block of the hand detection network based on a target expansion coefficient to obtain an expanded deep convolutional feature, the target expansion coefficient being smaller than a default expansion coefficient of the hand detection network; obtaining a weighted feature map obtained after the expanded depth convolution feature passes through an embedded SE architecture; performing multi-dimensional collaborative hand region directional extraction processing based on the weighted feature map to obtain a hand region of the pulse image data; and identifying the gesture type of the hand region. The stability and accuracy of gesture recognition under a low-computing-power platform are improved.

Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for the tracking of interacting hands using a sensor of a wearable multimedia device. In an embodiment, a method comprises: obtaining, with a sensor of a wearable multimedia device, a first frame of two-dimensional (2D) image data and a second frame of three-dimensional (3D) depth data; determining multiple hand regions in the first frame of 2D image data; determining a location of each hand region in the first frame of 2D image data; detecting at least one landmark in each detected hand region; generating a confidence score for each landmark detected in each detected hand region; determining 3D world coordinates for each landmark that is visible in the first frame of the 2D image data; tracking each landmark in 3D world coordinates; and determining an interaction with the hands based on the tracking of each landmark in 3D world coordinates.

The invention discloses a hand detection method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: processing an image by using a computer vision model to obtain a plurality of prediction frames and a left-hand confidence score and a right-hand confidence score corresponding to each prediction frame; marking a left-hand prediction frame and a right-hand prediction frame in the plurality of prediction frames; copying the target prediction box to obtain a first copy and a second copy, marking the first copy as a left-hand prediction box, and marking the second copy as a right-hand prediction box; performing feature modulation processing on the left hand prediction frame set to obtain key point coordinates corresponding to a hand region in a left hand prediction frame; and performing feature modulation processing on the right-hand prediction frame set to obtain key point coordinates corresponding to the hand region in the right-hand prediction frame. The technical problems of detection confusion and key point identity confusion caused by difficulty in accurately distinguishing the left hand and the right hand in hand detection and hand key point positioning are solved.

The invention discloses a hand movement track recognition method, system and device and a medium. The hand motion trail recognition method comprises the following steps: acquiring hand shot image data; the hand shot image data image is input into an improved YOLOv5 gesture detection model to obtain a hand area, the improved YOLOv5 gesture detection model comprises a backbone network, a neck network and a head network, C3 modules in the backbone network and the neck network are replaced with C3Rep modules, and a SimAM attention mechanism is introduced into the backbone network; and according to the hand region, obtaining a hand motion track. By adopting the embodiment of the invention, the hand region can be quickly positioned, and the hand motion track can be accurately identified and fitted, so that the track identification speed and precision can be improved, and reliable data support can be provided for related technologies.

The present invention proposes a method for parallel prediction of multiple regions of a hand, which relates to the technical field of image processing, and solves the problem that the current lightweight real-time capture and reconstruction system of a hand cannot handle the differences in features of different sizes in the hand region, and the prediction accuracy is poor. The method includes: taking a certain number of hand image samples at different hand angles to form an image data set; annotating each hand image sample in the image data set to obtain an image annotation sample; preprocessing the hand image samples; constructing a parallel prediction model of multiple regions of a hand; training the parallel prediction model of multiple regions of a hand; collecting hand images to be predicted, and inputting the hand images to be predicted into the trained parallel prediction model of multiple regions of a hand for processing. The present invention also proposes a system for parallel prediction of multiple regions of a hand, which can handle the differences in features of different sizes in the hand region, and can accurately predict the hand as a whole and the hand region, with high prediction accuracy.

This invention discloses a machine vision-based method and system for monitoring medication dispensing. The method involves acquiring a multi-view polarized image sequence and corresponding depth image sequence of the medication dispensing operation area; performing physical-level specular de-illumination on the multi-view polarized images and identifying transparent material regions to generate multimodal fusion data with transparency priors; detecting the operator's hand posture in real time to generate a dynamic occlusion mask; constructing and training a dynamic neural radiation field, incorporating a temporal de-occlusion prior loss during training to utilize unoccluded spatiotemporal information to recover the visual features of the area marked by the dynamic occlusion mask; and based on the trained dynamic neural radiation field, removing the hand region and rendering a de-occluded and specular-de-illumination virtual monitoring view. This invention solves the problems of target loss, inaccurate measurement, and poor interpretability caused by specular reflection, transparent objects, and dynamic occlusion.

The application discloses a gesture recognition method and a display device. The method uses hand posture confidence to determine whether to simplify the step of determining hand region position information. When the hand posture confidence corresponding to the previous frame of original color image is not less than the preset confidence, the step of determining the second hand information can be simplified, and the time for recognizing the gesture is shortened. The method comprises the following steps: when the obtained original color image is not the first frame of original color image, determining the hand posture confidence corresponding to the previous frame of original color image; if the hand posture confidence is not less than the preset confidence, predicting the hand region position information of the obtained original color image according to the first hand information obtained from the previous frame of original color image; removing the interference pixel points by using the hand region position information to obtain the first depth image; determining the second hand information by using the first depth image; and determining the hand posture information according to the first hand information and the second hand information.

The application discloses a method for determining the centroid of a human hand region and a display device. When a user performs 3D gesture interaction, an image collector collects an RGB image and a depth image. Based on the coordinates of the center point of the human hand detection frame in the RGB image, the coordinates of a target point corresponding to the center point of the human hand detection frame in the depth image are calculated. Based on the coordinates of the target point and the depth value of the human hand region where the target point is located, the coordinates of the centroid of the human hand region in the depth image are calculated. The method and the display device are based on non-aligned RGBD images, do not need complex calculation to realize the alignment function of the RGB image and the depth image, but greatly simplify the calculation method of the centroid of the human hand region in the depth image using the epipolar constraint method, reduce the search range of the corresponding feature points from the RGB image to the depth image, can timely determine the centroid of the human hand region, realize real-time interaction of the 3D gesture algorithm in an embedded terminal, and improve the immersive interaction experience of the user.