34results about How to "Accurate Gesture Recognition" patented technology

The embodiment of the invention provides electronic equipment and a method for carrying out projection display by utilizing the electronic equipment. The electronic equipment comprises a luminous source and a lens, wherein the luminous source comprises at least one visible light luminous source and at least one infrared luminous source; the visible light luminous source is used for emitting visible light; the infrared luminous source is used for emitting infrared light; and the lens is used for projecting out the visible light subjected to blending color mixing processing and the infrared light emitted by the infrared luminous source, so that a first projection image is protected and displayed in a projection region, wherein the visible light and the infrared light are respectively emitted into projection regions corresponding to each pixel of the first projection image. The visible light and the infrared light are respectively emitted into the projection regions corresponding to each pixel of the projection image transmitted by the electronic equipment provided by the invention, so the position of the projection region can be conveniently determined through infrared light rays reflected in the projection region, and the gesture recognition is accurately carried out.

The embodiment of the invention provides a control system for gesture recognition based on man-machine interaction equipment and a control method for the same. The system comprises an ultra-bandwidth module, a main control module, an antenna module and a communication level conversion module, wherein the main control module controls the ultra-bandwidth module to send a pulse radar signal to a human hand and generate an echo signal; the antenna module receives the echo signal and then sends the echo signal to the main control module for processing, so that gesture characteristics can be judged according to the echo signal; and then, the communication level conversion module converts the signal into a control signal of the man-machine interaction equipment according to the gesture characteristics, so that man-machine interaction is implemented. The control system is characterized in that the ultra-bandwidth module is used to send the pulse radar signal which can penetrate an obstacle and can avoid interference to the system, so that the gesture recognition becomes more accurate; and problems in the prior art that human body gestures cannot be detected and recognized accurately and thus the human interaction cannot be implemented can be solved.

The invention provides a gesture recognition method and system and relates to the technical field of computer vision. The gesture recognition method includes: performing multidirectional gradient extraction on an input image to form multiple different gradient images; recognizing the gradient images on the basis of a convolutional neural network pyramid to acquire an initial result set; combining the initial result set obtained by recognition to obtain a final recognition result of a gesture. Further, due to on the basis of the multidirectional gradient images describing the gesture image, ideas of a neural network and evidence theory combined recognition are integrated, so that gesture recognition accuracy is improved fully, and good recognition effect can be realized even if outside environment is similar to skin color scene and image quality is blurry.

The invention discloses a gesture identification method and system of a robot, and the robot. The method comprises steps of pre-acquiring pictures which contain different gestures and pictures which do not contain gestures so as to obtain a sample picture set; according to the sample picture set, making a detection sample set and a filtering sample set; according to the detection sample set, carrying out training so as to obtain an adaboost cascading gesture detector; according to filtering sample set, carrying out training so as to obtain a gesture identification convolution neural network; and identifying the acquired gesture pictures through the adaboost cascading gesture detector so as to obtain gesture identification results, and filtering the gesture identification results through the gesture identification convolution neural network so as to obtain a correct gesture identification result. According to the invention, by filtering the gesture identification results through the gesture identification convolution neural network, a gesture can be precisely identified under the complex background.

The invention discloses a virtual reality interaction system and method. The virtual reality interaction system comprises a smartwatch, a host machine and head equipment, wherein the smartwatch is used for acquiring user interaction information of a wearer; the host machine is in communication connection with the smartwatch and is used for receiving the user interaction information, analyzing and identifying the received user interaction information, taking the analyzed and identified user interaction information as interaction input information, processing the interaction input information and then outputting corresponding audio signals, video signals and vibrating signals; and the head equipment is in communication connection with the host machine and used for outputting video signals and audio signals. The virtual reality interaction system has the following beneficial effects that the immersive virtual reality experience is realized, and a visually immersive feeling is obtained; a rich interaction mode is realized; and vibration feedback is achieved, and the interaction experience is greatly improved.

The invention discloses a gesture-based photographing control method and device. The method comprises the following steps: identifying a gesture action of a user by use of an ultrasonic sensor in a mobile terminal; determining a shape indicated by the gesture action; and controlling a camera to photograph an image corresponding to the shape. By adopting the gesture-based photographing control method and device disclosed by the invention, the problems of the existing vision-based gesture identification method of low identification precision and poor user experience are solved, the gesture action is accurately identified, a photographing function is achieved according to the identified gesture action, and an effect of enhancing the interest and the user experience is achieved.

The invention belongs to the field of intelligent driving, and relates to a method and system for optimizing the human-computer interaction interface of an intelligent cabin based on three-branch decision. The method comprises the following steps: collecting gesture video in the cabin, preprocessing, and obtaining gesture image; segmentation of gesture image and background to obtain gesture regionimage; for multi-granularity expression, the multi-granularity feature of gesture region image being extracted by a convolution neural network; from coarse granularity to fine granularity, the imageclassification of each granularity gesture region to the conditional probability of each kind of gesture region being calculated, and the gesture recognition being accomplished sequentially by using three decision-making branches; the recognized gestures being semantically converted, and the human-computer interaction interface operating according to the result of semantic conversion. The best granularity is obtained by weighted summation, and the best granularity is used as the finest granularity. The invention not only can more accurately recognize gestures in the cockpit and execute gesturecommands, but also can reduce the interaction time of the cockpit man-machine interaction interface and provide more comfortable interaction experience for users.

The invention provides a space gesture control device based on machine vision. The space gesture control device comprises a main control module, a data acquisition module, a data processing module anda communication module, wherein the four modules are integrated and modularized, the control device is built based on an embedded platform, the main control module and the data processing module share a main control chip on a main board of the control device, and the communication module is integrated on the main board; the main control module controls the operation of other modules and monitorsthe operation state of the other modules in real time; the data acquisition module captures a hand image of a user through the monocular camera and transmits the hand image to the data processing module; the data processing module analyzes to obtain a control command and transmits the control command to the communication module, and the communication module sends the control command to controlledequipment; the device is accurate in gesture recognition and smooth in operation. The module is small in size, low in cost, high in integration degree and high in modularization degree, can be independently used for central control of a hardware system, can also be integrated in an overall platform, and is easy to build and maintain.

The embodiment of the invention discloses a gesture recognition method, intelligent equipment and a storage medium, and belongs to the technical field of human-computer interaction. The method comprises the steps of obtaining acceleration and angular velocity data of a to-be-recognized gesture, and performing filtering processing; calculating an acceleration resultant force time sequence accordingto the acceleration data; performing dimension unified processing on the acceleration resultant force time sequence and the angular velocity time sequence; according to a preset rule, binning the acceleration resultant force time sequence and the angular velocity time sequence after the dimension unified processing; and matching the acceleration resultant force and the angular velocity time sequence after binning with a pre-stored gesture template library, and determining the type of the gesture to be recognized. According to the embodiment of the invention, under the condition that the costof the sensor is not increased, the difference of the gesture data in space can be effectively regulated, and the difference caused by speed change or force change is reduced, so that efficient and accurate gesture recognition is realized. The gesture recognition method and the gesture recognition device can be widely applied to intelligent equipment with high user interaction experience.

The invention discloses a gesture recognition system and a gesture recognition method for a guessing and rowing fist game. Being used as a gesture recognition area, a gesture placing plate is arrangedon the gesture placing plate by hand, four independent capacitive sensors are arranged under the gesture place plate, four independent capacitive sensors are connected to an input end of a sensing chip, and an output end of the sensing chip is connected to a control microprocessor. Four independent capacitive sensors are arranged in a palm hand shape, wherein three independent capacitive sensorsare respectively arranged in a middle finger, a ring finger and a little finger position area of the palm, and another independent capacitive sensor is respectively arranged in a position area composed of a thumb and an index finger of the palm. The invention realizes non-contact gesture input and outputs accurate result, has high realization efficiency, high recognition accuracy and lower cost, and is very simple and effective.

A method and apparatus for gesture is disclosed. The gesture recognition method comprises the following steps: acquiring a plurality of gesture images with the same preset gesture type and different gesture angles, and merging the plurality of gesture images into a multi-channel image; the multi-channel images being input into a preset convolution neural network, and the preset non-linear featuresare determined. The preset non-linear features include the 2D spatial correlation features of the images and the correlation features of various pieces of representation information of gesture objects between different channels of the images. A preselection box is generated on the feature map, and the preselection box is used to predict the position of the gesture object in the image, and the gesture recognition result is obtained according to the preset non-linear features. The invention can avoid the influence of the external environment change or the gesture posture change on the gesture recognition, and can accurately recognize the gesture no matter how the gesture posture changes, how the gesture is shielded or how the illumination condition changes, so as to improve the accuracy ofthe recognition.

The invention relates to a gesture recognition method and device, an electronic device and a storage medium, and the method comprises the steps: obtaining a monocular to-be-recognized image in a videostream; recognizing the monocular to-be-recognized image of the current frame based on a completely trained detection network to obtain a human hand bounding box of the monocular to-be-recognized image; obtaining a current static gesture result according to the human hand bounding box based on a completely trained classification network, and storing the current static gesture result in a first result set; and according to the first result set, obtaining a second result set of continuous frames in the monocular to-be-identified image, obtaining a stable static gesture result including categoryinformation according to the second result set, and obtaining a dynamic gesture result. By means of the gesture recognition method and device, the problem that gesture recognition is low in accuracyis solved, and high-speed and accurate gesture recognition is achieved.

The invention provides a finger interaction recognition system based on MEMS laser scanning. The system comprises a laser light source module which is used for emitting the RGB three-color laser; a galvanometer module which is used for receiving the laser emitted by the laser light source module and scanning the projection pictures line by line; a laser receiving module used for receiving the laser reflected during laser imaging; a depth image acquisition module which is used for acquiring a depth image with the same size as the projection pictures based on a TOF algorithm; a fingertip detection module which is used for obtaining the position and depth of a fingertip by using a deep learning algorithm; a hand image segmentation module which is used for segmenting a hand image from the depth image and determining the fingertip position; a finger action recognition module which is used for performing finger action recognition based on a neural network and a support vector machine classifier; and a finger interaction module which is used for operating and controlling the content change of the projection pictures based on the determined fingertip position. The system is small in size,is capable of being operated by hands, is convenient to carry, can realize the accurate fingertip positioning and gesture recognition, and can operate and control the projection content in real time through hands.

The invention discloses sports underwear with the function of recognizing and evaluating a human trunk exercise posture. The sports underwear comprises a sports underwear body and an exercise posture recognition and evaluation module, wherein the exercise posture recognition and evaluation module comprises a shell, a power source, an MCU, an exercise posture recognition device, an exercise evaluation device, a memorizer and a warning device. The power source supplies power and is arranged in the shell, the MCU is arranged in the shell and located above the power source, and the warning device is arranged in the shell and located on one side of the MCU which is connected with the memorizer. According to the sports underwear, the exercise posture recognition and evaluation module is fixed on the sports underwear, the correction of an initial position is simple, and the trunk exercise posture of a user is recognized accurately; the exercise posture recognition and evaluation module has the function of measuring the strength of a part of muscles of the trunk, when the user completes the physical fitness exercise, muscle load is judged by measuring the current of the muscle, if the muscle load of the user is not up to the standard, an alarm is generated to prompt the user to increase the amount of exercise or adjust the posture, and thus a better exercise effect is achieved.

The invention discloses an unmanned aerial vehicle control method based on gesture recognition. The method comprises the following steps of (1) presetting a gesture recognition remote control unmannedaerial vehicle, (2) acquiring target gesture 3D point cloud data by using a first radar chip, (3) regenerating a target gesture image, (4) providing feature information of the target gesture image, (5) obtaining a target gesture action, and (6) comparing the target gesture action with the gesture sample so as to recognize a new gesture instruction, and (7) carrying out flight action control on the unmanned aerial vehicle body or carrying out shooting action control on the camera according to the new gesture instruction. The invention further discloses the gesture recognition remote control unmanned aerial vehicle for implementing the method. The unmanned aerial vehicle control method is relatively far in control range, relatively strong in obstacle avoidance function and suitable for long-distance flight control and shooting. According to the gesture recognition remote control unmanned aerial vehicle, the gesture action is recognized through the radar chip, and compared with existinggesture visual recognition, the gesture recognition distance, speed and precision are improved.

The invention discloses an automatic coupling assembling method based on machine vision, which comprises the following steps: left half couplings are conveyed, so that the left half couplings with upward notches are normally conveyed to the lower part of an elastic body assembling platform and aligned and assembled with elastic bodies to form coupling suites; right half couplings in different directions are conveyed, so that notches of the right half couplings located on a second assembling platform face the left side; the received elastic bodies are conveyed into assembly holes, and the elastic bodies are pressed into the left half couplings with upward notches through assembly pressing rods; the first assembling platform receives the coupling suites, and the second assembling platform receives the right half couplings; and a camera group is used for identifying the positions and orientations of the left half couplings, the elastic bodies and the right half couplings, and transmitting identification results to a left side platform, a right side platform and left and right assembly platforms.

The invention discloses a gesture method and device. The gesture recognition method includes: obtaining multiple gesture images with the same preset gesture type and different gesture angles, and merging the multiple gesture images into a multi-channel image; inputting the multi-channel image into a preset convolutional neural network, and determining the Set the feature map of nonlinear features, the preset nonlinear features include image 2D spatial correlation features and the correlation features of various representation information of gesture objects between different channels of the image; generate a pre-selection box on the feature map, and use the pre-selection box to predict gestures The position of the target in the image, and the gesture recognition result is obtained according to the preset nonlinear features. The present invention can prevent gesture recognition from being affected by changes in the external environment or gestures. No matter how the gestures change, how the gestures are blocked or how the lighting conditions change, the present invention can accurately identify gestures and improve the accuracy of recognition.

The invention discloses a gesture recognition device and method, and the device comprises a grabbing recognition mechanism which comprises a first arm which is configured to move along with an index finger; the second arm is hinged to the first arm and is configured to move along with the thumb; the offset detection mechanism is used for detecting the movement offset of the first arm relative to the second arm; and the processing module is in communication connection with the offset detection mechanism and is used for receiving the movement offset sent by the offset detection mechanism and calculating the opening degree of the first arm and the second arm according to the movement offset. The invention discloses a gesture recognition device. According to the human engineering principle, when fingers execute grabbing actions, finger postures can be changed, the arms capable of moving along with different fingers are arranged, the opening degree between the fingers is reflected, finger actions can be accurately recognized, the influence of accumulative errors of a sensor is avoided, and the detection accuracy is higher.

The invention discloses an automatic coupling assembly line based on machine vision. The assembly line comprises a left side platform, a right side platform, an elastomer assembling table, a left-right assembling platform and a camera group, the left side platform is used for conveying left half couplings, enabling the left half couplings with upward gaps to be normally conveyed to the lower part of the elastomer assembling table, and aligning and assembling with an elastomer to form a coupling suite, the right side platform is used for conveying the right half couplings in different directions so that gaps of the right half couplings located on a second assembling platform face the left side, the elastomer assembling table receives the elastomer and conveys the received elastomer into an assembling hole, the elastomer is pressed into the left half couplings with the upward gaps through an assembling pressing rod, the left-right assembling platform comprises a first assembling platform and the second assembling platform, the first assembling platform is used for receiving the coupling suite, the second assembling platform is used for receiving the right half couplings, and the camera group is used for identifying the positions and directions of the left half couplings, the elastomer and the right half couplings and conveying identification results to the left side platform, the right side platform and the left-right assembling platform.

The invention relates to the field of direct water dispensers, in particular to an AI interactive direct water dispenser based on the Internet of Things, which comprises a water accumulation structure, a direct water dispenser body, an interactive screen, a storage structure, a processing structure, a storage structure, an information acquisition structure and a sealing structure, a sealing structure used for sealing is arranged at the top end of the direct water dispenser body, a storage structure used for storing drinking water is clamped with the sealing structure, and therefore the drinking water can be conveniently stored through the storage structure, the direct water dispenser can be suitable for different environments, and meanwhile the drinking water can be stored and taken more conveniently and rapidly. The bottom end of the direct water dispenser body used for purifying a water source is provided with the water accumulation structure used for accommodating water flow, and the bottom end of the direct water dispenser body is provided with the accommodating structure which is communicated with the water accumulation structure and used for accommodating sweeps, so that thetea water sweeps can be conveniently accommodated through the accommodating structure, and water drinking is more convenient and faster.

The invention discloses a gesture recognition method and device based on multi-point TOF, and belongs to the technical field of information processing. According to the main technical scheme, the method comprises the following steps: starting an infrared time-of-flight TOF sensor; recording an entering moment when a hand to be measured enters the TOF sensor and an exiting moment when the hand to be measured exits the sensor, and calculating a signal phase calculation moment according to the entering moment and the exiting moment; and calculating a motion deviation angle of the hand to be measured relative to the central coordinate of the plane where the TOF sensing detection device is located according to the signal phase calculation moment, and determining the motion direction of the handto be measured according to the motion deviation angle and the signal phase calculation moment.

The invention discloses a multi-point touch gesture recognition method and a mistaken touch prevention method, and the gesture recognition method comprises the steps: defining a canvas touch management mode, and dividing canvas touch into gesture touch and drawing touch; defining a state conversion process in a finite state machine (FSM) in a canvas mode; judging the touch number and the touch type according to the touch area and the touch pressure intensity; adding an ID field to each touch point, and tracking the change of the touch state; eliminating the touch jitter by moving the detection threshold according to the change of the tracking touch state; in combination with the set ID, carrying out touch grouping and realizing multi-point touch gesture recognition. According to the invention, drawing with multiple single fingers and eraser operation with more than three fingers can be supported at the same time, and the situation that the system can only work to identify single touch operation is broken through; moreover, through touch grouping, simultaneous single-finger drawing of multiple persons and simultaneous erasing operation of more than three fingers of multiple persons can be realized.

The invention discloses a data glove gesture recognition algorithm based on mathematical statistics, and relates to the technical field of artificial intelligence. The method comprises the steps thata sensor.getData () method is used for a worn data glove, the same gesture is divided into a natural state and a stretching state to collect sensor angle values respectively, and the values are storedin two documents respectively; numerical values in the text document are read and stored in arrays a1 and b1, a2 and b2,..., a20 and b20, ai = (ai, 1, ai, 2, ai, 3,..., ai, 24), and bi = (bi, 1, bi,2, bi, 3,..., bi, 24). According to the method, the deviation degree and the linear relation between the randomly acquired gesture and the gesture in the template library are judged by calculating thestandard deviation and the correlation coefficient. The gesture most similar to the randomly acquired gesture is found out in the template library. Through two times of screening, the screening valueprecision is gradually improved, so that gesture recognition is very accurate. The gesture recognition based on the data glove can eliminate the interference of the external environment.

The embodiment of the invention provides a gesture recognition system and method and electronic equipment. The gesture recognition system includes: an antenna array including a plurality of millimeter wave radar transceivers configured to transmit at least one set of ranging signals and receive echo signals reflected by a target object; the data acquisition unit is configured to acquire the echo signal and cache the echo signal; and the processing unit is configured to process the cached echo signal to obtain movement information of the target object, and the movement information of the target object is used as gesture information. According to the gesture recognition system provided by the embodiment of the invention, the antenna array is simple in structure, the millimeter wave radar transceiver is adopted, the structure is simple, the size is small, the cost is low, gesture recognition can be simply realized, and the recognition rate is high.

The invention discloses a fire rescue unmanned aerial vehicle system capable of replacing a battery in the air and a control method thereof. The method comprises the following steps: S1, presetting a system; s2, the first unmanned aerial vehicle executes the task; s3, the first unmanned aerial vehicle enters hovering when the electric quantity is low; s4, the second unmanned aerial vehicle flies to the position under the first unmanned aerial vehicle, and the first battery tray and the host battery jar are aligned; s5, switching the power supply of the first unmanned aerial vehicle to the standby battery, and closing the magnetic lock; s6, the external battery falls to the first battery tray, and the second battery tray is aligned with the host battery jar; and S7, sending the replacement battery into the host battery jar, electrifying the magnetic lock, and adsorbing the replacement battery into the host battery jar. Through the synchronous improvement system and the control method, the battery is magnetically connected and fixed or demagnetized, dropped and separated through the magnetic lock, the air battery of the unmanned aerial vehicle is replaced, the cruising ability of the unmanned aerial vehicle can be effectively improved, and the time for replacing the battery back and forth by the unmanned aerial vehicle is shortened.