111 results about "Gesture tracking" patented technology

The invention discloses a man-machine interactive system and a real-time gesture tracking processing method for the same. The method comprises the following steps of: obtaining image information of user side, executing gesture detection by a gesture detection unit to finish separation of a gesture and a background, and automatically determining a smaller rectangular frame which surrounds the hand as a region of interest in the image information through a vision algorithm; calculating the hand profile state of each frame in a video sequence by a gesture tracking unit; executing validity check on hand actions according to the calculated hand profile state, and determining the gesture action finished by the user; and generating a corresponding gesture action control instruction according to the determined gesture action, and making corresponding feedbacks by a three-dimensional user interface according to the gesture action control instruction. In the system and the method, all or part of gesture actions of the user are sensed so as to finish accurate tracking on the gesture, thus, a real-time robust solution is provided for an effective gesture man-machine interface port based on a common vision sensor.

Provided is an apparatus, method, and medium for tracking a gesture, wherein, after a user is positioned approximately, a specific part of the user's body is tracked continuously. The gesture-tracking apparatus includes a positioning unit positioning a user appearing in an inputted image and a target-tracking unit tracking a first body part of the user.

The invention provides a gesture tracking method. The gesture tracking method includes steps of designing gesture appearance models including image description modes for tracking prediction and prediction verification; acquiring initial states, positions and size information of targets by gesture detection; initializing a tracker for the targets according to the initial states by details of initializing the appearance models, namely initializing image description templates for tracking prediction and prediction verification, and initializing types, states and visibility of gestures; tracking; processing the states and the visibility of the targets according to information of the tracker so as to make a final estimate; and judging the visibility of the targets, starting the process of gesture detection again to acquire a tracking target if information is lost permanently, otherwise, continuing tracking the targets. The invention further provides a gesture tracking system. The gesture tracking method and the gesture tracking system have the advantages of simplicity, rapidness and stability.

The invention discloses a reentry aircraft finite time control method based on a disturbance observer and relates to the reentry aircraft finite time control method. The reentry aircraft finite time control method belongs to the field of aircraft control technology. The reentry aircraft finite time control method comprises the following steps: a first step, establishing a reentry aircraft dynamic model, presenting a finite time gesture tracking task; a second step, performing feedback linear processing on a model which is established in the first step; a third step, providing a finite time control law; a fourth step, providing a finite time disturbance observer for estimating system uncertainty and outer disturbance; and a fifth step, providing a finite time global sliding mode control method based on the disturbance observer, and effectively improving tracking precision of a gesture control system through substituting an estimated disturbance value into the control law. The reentry aircraft finite time control method has functions of improving system error convergence speed, preventing a singular problem and improving uncertainty of the controlled system to the parameter, global robustness of outer disturbance and tracking precision.

The invention discloses a method for detecting an illegal driving behavior based on hand gesture tracking. The method comprises the following steps of: 1, reading an image from a monitoring video; 2, pre-processing the read image, and particularly performing gray conversion, image filtration, edge extraction, contour enhancement and the like; 3, positioning a steering wheel; 4, intercepting a corresponding interested area by using the steering wheel as the center; 5, extracting features of the intercepted area; and 6, classifying and identifying the extracted features, and judging whether hand gesture belongs to the illegal driving behavior. By the method, the illegal behavior of a driver in a driving period can be effectively determined, and a traffic accident which is caused by the illegal operation of the driver can be effectively avoided.

The invention provides a gesture tracking method for a VR headset device, which comprises the following steps of: acquiring a plurality of training images; separating hand depth images; marking a three-dimensional gesture, and forming original point cloud; calculating a normal vector and a curvature, and carrying out mean removing normalization; setting up a CNN network, wherein an input end of the CNN network is used for respectively inputting multiple normal vectors, curvatures and hand depth images, and an output end of the CNN network is used for outputting three-dimensional coordinates of a plurality of joint points including a palm center; using the trained CNN network as a feature extractor of the three-dimensional gesture, acquiring real-time action depth images by a depth camera, extracting and processing normal vector, curvature and hand depth image information of the three-dimensional gesture which the real-time action depth images comprise by the feature extractor, outputting the three-dimensional coordinates of a plurality of joint points including the palm center, and carrying out tracking on the identified three-dimensional gesture. The invention further discloses a VR headset device. The gesture tracking method and the VR headset device which are provided by the invention fuse three-dimensional feature information, and have the advantage of high model identification rate.

The invention discloses a method for tracking deformable hand gesture based on video streaming, comprising the steps of: obtaining a frame image, and extracting a sub-image containing a human hand from the obtained frame image; selecting feature tracking points from the sub-image containing the human hand, and initializing a continuously self-adaptive mean shift tracker by the sub-image containing the human hand; performing optical flow calculation on the selected feature tracking points to serve as a local tracking result, and synchronously overall tracking the human hand by the continuouslyself-adaptive mean shift tracker to obtain a global tracking result; updating the feature tracking points; and adopting the result of the optical flow tracking as the final output result of the deformable hand gesture. The method for tracking the deformable hand gesture based on video streaming can be used for tracking the human hand with randomly deformable hand gesture and enabling human-computer gesture interaction to operate in a more comfortable manner. According to the invention, the tracking can be performed aiming at the randomly deformable hand gesture, the interference from change of a background and a large area of complexion is eliminated, and the real-time robust hand gesture tracking is achieved.

The invention discloses a relocation method and device for a camera gesture tracking process, equipment and a storage medium, and belongs to the field of augmented reality. The method comprises the following steps that: obtaining a current image collected behind the ith tagged image in a plurality of tagged images; when the current image conforms to a relocation condition, selecting a target key frame image selected from a key frame image library, wherein the key frame image library stores the image information of at least one key frame image, and the key frame image is an image which is cached in the camera gesture tracking process and successfully carries out first relocation than other images; carrying out second relocation on the current image than the target key frame image; and according to the location result of the first relocation and the location result of the second relocation, carrying out calculation to obtain a camera gesture parameter when the camera collects the currentimage.

A three-dimensional (3D) human-computer interaction system that supports mouse operations through the motion of a finger and an operation method thereof are provided. In the provided system and method, a multi-view video recording device captures an image of a finger of an operator, and has an information connection with an electronic information device through an interaction handling program. After the interaction handling program is executed, a CPU of the electronic information device performs operations such as computation, synthesis, image presentation, gesture tracking, and command recognition on the captured image to interpret a motion of the finger of the user. Accordingly, the user may perform operations on an operation interface of the electronic information device by using the finger in a 3D space in a noncontact mode. The present invention is particularly applicable to application software with 3D space operations as it can perform 3D interaction with the application software.

A method and system for recognizing complex patterns of motion and gesturing is disclosed. In one embodiment of the invention a pair of wrist (or ankle) bands senses motion which is analyzed to determine user movements in sagittal, frontal, and transverse planes. An optional front facing camera may be used to augment the data stream collected by motion sensing bands worn by the user. Games and training applications used in conjunction with the motion sensing system provide appropriate feedback, incentives, and scoring to the user via visual, audible, and haptic mechanisms.

The invention discloses a man-machine interaction system device on the basis of gesture recognition and a method for operating the man-machine interaction system device. The man-machine interaction system device comprises a single camera, a PC (personal computer), a PLC (programmable logic control) board and physical application equipment. Man-machine interaction programs are installed in the PC. The method is implemented by the aid of a gesture input module, a fingertip positioning module, a gesture tracking module and a keyboard and mouse mapping module. According to the man-machine interaction scheme on the basis of gesture recognition, the man-machine interaction system device and the method have the advantages that the problem of limitation of man-machine interaction modes which need to be sensed by a large quantity of hardware equipment at present can be solved, and accordingly man-machine interaction can be naturally and conveniently carried out.

The invention discloses a gesture tracking method of any hand shape. The method comprises the following steps: (1), initializing a background model; (2), initializing a position area of a hand; (3), obtaining a statistical histogram of a target area according to the position area of hand; (4), according to the statistical histogram, carrying out histogram back projection on an image that is read in at the current time so as to obtain a skin color probability graph; (5), difference processing is carried out on a current frame, a previous frame, and the background model respectively so as to obtain a difference image; (6), on the basis of the difference image, all pixel points are marked with different types so as to obtain a foreground probability graph; (7), a motion skin color probability graph based on the foreground probability graph and the skin color probability graph; (8), carrying out clustering on the motion skin color probability graph and localizing a precise position of the human hand; and (9) updating the background model according to a tracking result. According to the invention, human hand tracking can be carried out simply and effectively; and the requirement of the hand shape of the user is low. Moreover, the computation complexity is low; and the humanization effect is realized.

The invention discloses a method and a system for assisting yoga exercise. The method comprises the following steps: using an intelligent wearing device for detecting a user data when a user exercises yoga action, wherein the user data comprises a gesture tracking data, a heart rate data and a blood oxygen data; comparing the user data with a standard data of the yoga action; sending a prompt message if the user data is not corresponding to the standard data. According to the method, the gesture tracking data, the heart rate data and the blood oxygen data of the user in the process of exercising the yoga action are measured, the measured data is compared with the standard data, and when a comparison result is not corresponding, the exercising action of the user is in fault and the prompt is timely given out and the incorrect exercising manner of the user can be corrected, and thus, the problem that a beginner is difficult to correctly exercise yoga under the condition of being short of yoga coach guidance is solved.

The invention provides a gesture tracking method based on a population randomly-scrambled multi-target genetic algorithm. The method includes the following steps that arm skeleton information is acquired; a mechanical arm motion model is established; multi-target genetic algorithm energy and trajectory optimization is conducted; and gesture tracking is performed. According to the gesture tracking method based on the population randomly-scrambled multi-target genetic algorithm, the population randomly-scrambled multi-target genetic algorithm is introduced to achieve energy optimization, final position accuracy optimization and human body arm tracking trajectory optimization. Experimental results show that the system man-machine interaction is good, energy consumption of a mechanical arm is low, tail-end accuracy is high, and the human body arm tracking motion effect is good.

The invention discloses a gesture tracking recognition joint strategy method oriented to deskVR, At first, that hand gesture public data set ICVL is train offline by the convolution neural network, get a good robustness, models with high accuracy and fast recognition speed, and by testing the balance point Rate of accuracy and speed of the combined method with synthetic data, Then the depth imagesare captured by the depth camera in real time. After a series of preprocessing, the images are transferred into the gesture recognition method based on the fast model or the five-region convolution neural network model according to the proportion of the hand images, and the 3D information of the recognized joints is returned, and then the 3D human body model is mapped. The recognition method of the invention has good interactive function under the deskVR application environment, and performs well in recognition rate, operation speed, universality, accuracy and the like.

The invention provides a monocular three-dimensional gesture tracking method. The method comprises the steps of training a hand detection model and a skeleton point recognition model, starting a handdetection model and a tracking module according to the detection number of the hands in the previous frame of image, carrying out skeleton point identification on the region of interest of the currentframe in the Trackhand through a skeleton point identification model, and carrying out smooth filtering processing on the identified skeleton points; counting data about the position and posture of the head in each frame of image; storing the data of the head into a queue Trackhead of the tracking module in real time; determining three-dimensional skeleton coordinates of the skeleton points subjected to smooth filtering processing in combination with head data in Trackhead; rendering the three-dimensional skeleton coordinates to complete gesture tracking. According to the invention, one monocular camera is used for replacing two infrared binocular cameras, the cost is reduced, even if the two monocular cameras are installed, the cost is lower than that of one infrared binocular camera, the overall energy consumption and heat dissipation are reduced, the overall mass of head wearing is reduced, and the comfort of head wearing is improved.

The invention relates to a gesture tracking and accurate fingertip positioning method based on Kinect. According to the method, the tracking quality of hand disordered motion and the accuracy of fingertip positioning are improved. Taking Kinect as an example, the method comprises: reading depth information from Kinect, and completing shielding repair of hand skeleton information through assistanceof skeleton data and forward kinematics; after the optimized bone joint point information is obtained, conducting smooth processing on the tracked palm motion information through a Kalman filtering algorithm; then, extracting a hand region from the depth image by using the real-time palm coordinate information and adopting a connected region analysis algorithm of firstly performing coarse extraction and then performing fine extraction; and finally, obtaining a convex hull of the hand contour, filtering out fingertip approximate points by adopting a method of analyzing a geometrical relationship between the convex hull and a palm center point, and calculating a curvature value to obtain accurate fingertip points. Therefore, real-time tracking of the hand is realized, and the accuracy of fingertip positioning is improved according to experimental result analysis.

The invention discloses a multi-rotating direction SVM model gesture tracking method based on HOG characteristics. Images continuously grabbed by a Web camera serve as input images, and the set gesture initial detecting positioning and later tracking positioning are carried out on the frames of the images. A HOG+Multi-SVM detecting module is used for obtaining the set gesture detected and positioned in front several frames of gesture images from the Web camera; under the situation that the image frames include the set gesture, a gesture tracking module of a multi-rotating direction SVM model based on the HOG characteristics is started for tracking positioning; once the tracking module loses the tracking for the set gesture or the gesture is moved out of the shooting range of the Web camera, the HOG+Multi-SVM detecting module is started again till the set gesture can be continuously detected and positioned successfully. The speed for positioning the set gesture can be effectively increased, and meanwhile the positioning precision for the set gesture is also improved.

The invention discloses a film-type whole automobile external ambient light intelligent control system with gesture control. The film-type whole automobile external ambient light intelligent control system comprises a main driving position driver hand position and gesture tracking and sensing module, a nanometer touch film, a micro control unit (MCU), an automobile interior temperature sensing module, an automobile exterior temperature sensing module, an automobile interior light intensity sensing module, an automobile exterior light intensity sensing module and an ambient light control panelsystem, wherein the MCU controls the nanometer touch film to adjust light transmission according to signals fed back by the main driving position driver hand position and gesture tracking and sensingmodule, the automobile interior temperature sensing module, the automobile exterior temperature sensing module, the automobile interior light intensity sensing module and the automobile exterior lightintensity sensing module. The intelligent control system constructed by the invention aims at energy saving, emission reduction, intelligence, high efficiency, safety and comfort. The system can be used for adjusting the light transmittance so as to achieve the adjustment of the automobile interior temperature and light intensity during parking or running of an automobile, so that the automobilecan better protect the privacy of a driver and passengers.

The invention discloses a real-time gesture tracking method based on a cascade deep neural network. The method comprises: image data are obtained by a TOF camera and a color camera; an image preprocessor carries out preprocessing on the image data; essential characteristic extraction is carried out on the data after preprocessing by using a primary characteristic extractor; a cascade artificial neural network system carries out advanced characteristic abstracting processing; a mode matching device carries out mode matching according to the advanced abstract characteristics after characteristic abstract processing; and then an attitude processing center calculates all positions of twenty six nodes of a hand to obtain a hand attitude and spatial position data, and the data are transmitted to a computer application by a gesture attitude data flow. Rapid characteristic extraction, matching, and attitude calculation are carried out on image information of the human hand to guarantee the high stability, precision, and real-time performance of the calculated gesture attitude.

The invention discloses a gesture speed estimating method for a three-dimensional interaction interface. The method includes: in order to reflect different speeds, using a data glove to determine the basic relationship between gesture speed and gesture image change; building speed estimation models in a graded manner according to the relationship; estimating hand moving parameters according to gesture images to obtain real-time hand moving seed, and allowing the gesture speed to be consistent with the hand moving speed. The method has the advantages that compared with a gesture tracking algorithm based on behavior models, average operation time is lowered by 26%; the total error of the tracking precision as compared with the data glove at different moving speeds is 10%; the model is already applied to the three-dimensional interaction interface, and testing of a prototype system shows that the model is good in user experience.

The invention discloses a gesture tracking method and device, and the method comprises the steps: enabling a wearable wristband to be worn at a wrist, enabling the surface of the wearable wristband tobe provided with optical pattern marking points, and enabling an inertial navigation sensor to be built in the wearable wristband; capturing the motion state of the optical pattern mark point in thespace in real time, obtaining an optical tracking image, and obtaining inertial navigation data of the wearable bracelet through the inertial navigation sensor; according to the optical tracking imageand the inertial navigation data, obtaining 4DoF information of the wearable bracelet through a computer vision technology, and the 4DoF information representing position movement information of thehand in the x-axis direction, the y-axis direction and the z-axis direction of the three-dimensional space and rotation information of the hand in the z-axis direction; and calculating other 22DoF information of the hand through the 4DoF information, thereby obtaining the position and posture information of the hand. According to the invention, the tracking precision of the hand position and posture can be improved.

The invention discloses a hand posture estimation and tracking method based on depth data. According to the method, the hand parameter estimation is obtained through a hand depth image obtained through a depth sensor and by using a semi-supervised hand posture estimation method combining a variational autoencoder (VAE) and a generative adversarial network (GAN), and then the hand action parameterization time series data is obtained by using a gesture tracking method based on a symbolic distance function (SDF). In the practical application, the method is high in calculation efficiency and strong in real-time performance of posture estimation, can obtain a gesture estimation result with higher precision and the hand rapid tracking with better robustness under the constraint of a small number of labeled samples and a large number of non-labeled samples, and the calculation resources and the time can be saved.

The invention discloses a KCF long-term gesture tracking method integrated with an SLIC algorithm, and the method comprises the steps: 1) building a gesture training data set, extracting and carryingout the offline training of an SVM model of a super-pixel block, and obtaining a coarse classification model of gesture detection; 2) constructing a foreground-background dictionary, and designing a similarity function of a KNN algorithm by combining the FHOG feature and the CN feature so as to finish fine classification of gesture detection; 3) obtaining a gesture detection model through the coarse classification model of gesture detection and the fine classification of gesture detection, and detecting a target by using the gesture detection model to obtain a detection box of a target gesture; 4) estimating a rectangular frame of the most suitable target gesture by using the designed target scale estimator; and 5) designing a confidence function, and determining whether the current tracking result is credible to realize gesture tracking by comparing the similarity between the current frame tracking result and the previous frame tracking result. The method has the advantages of low algorithm complexity, high tracking precision and high robustness, and is suitable for real-time application occasions.

Method for controlling a combined eye and gesture tracking system (100), wherein the method comprises providing a graphical user interface (102) to a user including a plurality of graphical items (110) displayed on the graphical user interface (102, 300), displaying a graphical pointer (120) on the graphical user interface (102, 310), detecting a user eye gaze (210) associated with the eyes of the user (320), detecting a displacement user gesture (230) associated with a body part of the user (330), and controlling a rate of movement of the graphical pointer (120) on the graphical user interface based on the displacement user gesture (230) and the user eye gaze (210, 340).