17102 results about "Video camera" patented technology

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

The present invention is a Large Language Model-Based AI-powered interactive doll that provides personalized content, education, companionship, entertainment and emotional support to children. The doll connects to a large language model (LLM) and uses a microphone, video camera, touch screen and speaker to interact with the child. The integrated LLM provides real-time video analysis capabilities. The accompanying app provides a full suite of customization options for parents to tailor the doll's interactions to their child's needs and preferences. The doll provides a wide range of content, including educational content, homework assistance, entertainment, AI art, video and music generation, companionship and emotional support, and can suggest activities, tell personalized interactive stories, assisting with language learning, play games, and interact with the child in various other ways. The doll can also be used as a monitor, allowing parents to listen in on the child's interactions, and in another embodiment, to livestream video via an internal camera.

A system includes a magnetic stimulation coil; and a machine vision navigation device comprising a camera configured to capture successive still images and / or to stream video indicating position of the magnetic stimulation coil. The machine vision navigation device is configured to determine, based on the successive still images and / or the video, the position of the magnetic stimulation coil relative to a target treatment area of a patient, and cause, based on the position of the magnetic stimulation coil relative to the target treatment area of the patient, display of an indication of the position of the magnetic stimulation coil relative to the target treatment area of the patient.

The invention discloses a road defect detection method based on heavy parameter multi-scale fusion, and belongs to the technical field of target detection in a deep learning neural network, and the method comprises the steps: selecting a data set of road defects from a public data source, and dividing the data set into a training set, a verification set and a test set; a lightweight road defect detection neural network model based on heavy parameter multi-scale fusion is constructed, the model adopts a heavy parameterization technology and a multi-scale feature fusion strategy, and various road defects in the image can be identified and positioned; the constructed neural network model is trained by using the divided training set and verification set, an optimal lightweight road defect detection model is obtained through index evaluation, then reasoning acceleration of the lightweight detection model is carried out, and the lightweight detection model is embedded into an edge computing terminal for deployment; a vehicle-mounted camera is used for collecting image data, collected road images are input into an edge computing terminal to be processed, and types and positions of road defects existing in the vehicle-mounted images are output.

This application relates to the field of photographing technologies, and discloses an image processing method and an electronic device, so as to dynamically adjust LUTs in a process of photographing or visual recording, and enrich display effects of photographing or visual recording. The electronic device obtains a first image, where the first image is an image captured by a camera of the electronic device, and the first image includes a first shot object; the electronic device determines a first scenario corresponding to the first image, where the first scenario is used to identify a scenario corresponding to the first shot object; the electronic device determines a first LUT based on the first scenario; and the electronic device processes the first image based on the first LUT to obtain a second image, and displays the second image, where a display effect of the second image corresponds to the first LUT.

A system is provided for determining a target neurostimulation device position. The system comprises: one or more cameras arranged to capture one or more images of a body part of a subject; a neurostimulation device arranged to provide a neurostimulation to the body part of the subject; and a computer comprising a processor and a computer-readable medium arranged to: receive the one or more images from the one or more cameras; determine, by the processor using computer vision to process the one or more images, a three-dimensional (3D) target neurostimulation device position located relative to the body part of the subject; wherein the target neurostimulation device position comprises a 3D translational position metric indicating an area of operation of the neurostimulation device located relative to the body part of the subject. The described system aims to provide a non-invasive manner of providing neurostimulation which reduces the complexity of resources required, the time taken and the level of skill required, while providing a greater level of consistency of treatment provided.

Systems and methods for generating static and articulated 3D assets are provided that include a 3D autodecoder at their core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space, which can then be decoded into a volumetric representation for rendering view-consistent appearance and geometry. The appropriate intermediate volumetric latent space is then identified and robust normalization and de-normalization operations are implemented to learn a 3D diffusion from 2D images or monocular videos of rigid or articulated objects. The methods are flexible enough to use either existing camera supervision or no camera information at all—instead efficiently learning the camera information during training. The generated results are shown to outperform state-of-the-art alternatives on various benchmark datasets and metrics, including multi-view image datasets of synthetic objects, real in-the-wild videos of moving people, and a large-scale, real video dataset of static objects.

The invention provides an airport terminal video digital twinning method, device, equipment and medium, and the method comprises the steps: firstly obtaining data collected by a plurality of sensors, then constructing a high-precision airport terminal static three-dimensional model, and taking a world coordinate system as a reference, a mapping relation among a two-dimensional image coordinate system, an airport terminal static three-dimensional model coordinate system and a camera coordinate system is constructed, an airport terminal space initial layout is obtained, two-dimensional layout estimation is used as a key point, image textures are reconstructed, registration from the two-dimensional image textures to an airport terminal static three-dimensional model under a single camera is achieved, and the registration result is obtained. And finally, calculating a suture line in a fusion rendering process, and fusing a multi-camera two-dimensional image with the constructed static three-dimensional model to realize the three-dimensional panoramic digital twinning of the terminal building. Through the scheme of the invention, three-dimensional live-action visualization under the fusion of multiple cameras, structured data and the terminal indoor model in a large scene of the terminal is realized, comprehensive real-time monitoring is realized, and safe operation of an airport is guaranteed.

Aspects of the subject disclosure relate to a vehicle camera system for view creation of view locations. A device may include a processor that obtains first data and second data from different cameras of a vehicle. The first data may include an image representation of a scene in a first field of view being observed from a first perspective view and the second data may include an image representation of the scene in a second field of view being observed from the first perspective view in a second field of view. The processor may modify the first data and the second data by a perspective transformation to adjust the image representation to a second perspective view. The processor may create a stitched image representing the scene in a combined field of view observed from the second perspective view by stitching the modified first data with the modified second data.

The invention discloses an AR home experience method in a large scene. On the basis of combination of a natural feature identification-based three-dimensional registration method and a binocular tracking positioning and local mapping method, the camera attitude is estimated by using feature points of a real-time scene and corresponding three-dimensional points thereof under the binocular trackingpositioning and local map construction technology. According to the mode, on-site environment features shot in real time are used as recognition tracking objects, a virtual home model can still be normally positioned and tracked under the condition that no identification graph exists, the problems that an existing AR home experience application is small in use range and poor in stability are solved, and therefore the AR home experience of virtual and real fusion can be met in a wider range and more truly.

A system and method for perceiving a 3-D environment by using a camera and a radar sensor are disclosed. In order to generate a 3-D feature map that is used to perceive an environment through the fusion of a camera and a radar sensor, the method of perceiving a 3-D environment may include extracting a two-dimensional (2-D) feature map from an image obtained by the camera and transforming the 2-D feature map into a feature map in a 3-D space by using first distance information extracted from the image and second distance information measured by the radar sensor.

In some examples, a system may determine a plurality of candidate routes to a destination location for a vehicle, and may segment each candidate route into multiple road segments. The system determines one or more compression methods to be utilized for compressing images captured by one or more vehicle cameras on board the vehicle while the vehicle is traversing at least one road segment of at least one route of the plurality of candidate routes. The system selects, for the vehicle, a first route based at least on the one or more compression methods. The system sends, to the vehicle, information related to the first route and the one or more compression methods. The vehicle may utilize the one or more compression methods for compressing the images captured by the one or more vehicle cameras during traversal of the first route.

An AI based inventory control system. A first moveable drawer, the first moveable drawer moveable from a first position to a second position. A first camera arranged to generate an image of an inventory item contained within the first moveable drawer. An image processor in operative communication with the first camera, the image processor configured to process the image generated by the first camera. An AI engine performing recognition on the processed image received from the image processor.

The invention provides a camera extrinsic parameter calibration method, an image stitching method and an apparatus thereof. The calibration method can include: acquiring a plurality of images captured by the camera based on a plurality of sets of extrinsic parameters to be calibrated, wherein each of the plurality of sets of extrinsic parameters to be calibrated include a horizontal rotation angle and a vertical rotation angle; obtaining at least one matched image pair based on feature point matching among the plurality of images, wherein each matched image pair includes matched feature point pairs with a quantity satisfying a threshold condition; and calibrating the plurality of sets of extrinsic parameters to be calibrated according to a first difference representation between coordinate representations of each matched feature point pair of each matched image pair in a reference coordinate system.

The invention discloses a system for improving the applicability of an intelligent camera, and relates to the technical field of intelligent camera shooting. RGB, infrared and depth sensors are used for cooperative sensing, a dynamic inter-frame interpolation algorithm is used for complementing video data, target trajectory prediction is carried out based on a target motion vector analysis algorithm in combination with a recurrent neural network LSTM + Kalman filtering, and the applicability of the intelligent camera is improved. Meanwhile, three-dimensional scene modeling and shielding recovery are introduced, and environment information complementation of a dead angle area is optimized; through a cross-camera target identity re-identification ReID algorithm, seamless tracking is carried out among multiple cameras by utilizing appearance characteristics, gait characteristics and biological characteristics of a target, and meanwhile, PTZ camera control is optimized in combination with reinforcement learning, and the angles of the cameras are dynamically adjusted; a generative adversarial network GAN based on space-time consistency is adopted, historical video data and environment modeling are combined, missing pictures in visual dead angles are intelligently complemented, and the continuity and authenticity of monitoring pictures are improved.

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

A computer-implemented method is provided for a mobile device to detect, by a camera of the mobile device, a plurality of checks; determine, by a processing unit of the mobile device, that the image of the plurality of checks is of sufficient quality; instruct, by a display of the mobile device, a user to take a photograph of the plurality of checks; crop, by the processing unit, the photograph of the plurality of checks into a plurality of images, wherein each of the plurality of images contains one of the plurality of checks; and transmit, by a transmitter of the mobile device, the plurality of images to a server via a network. The plurality of images may be transmitted individually (i.e., one at time), or alternatively, collectively and in one payload.

The invention discloses a construction site intelligent safety management system and method based on a BIM technology, and relates to the technical field of constructional engineering, and the method comprises the following steps: S100, building a three-dimensional digital model of a construction site by using BIM software, and collecting construction site data by deploying a video camera, personnel positioning equipment and an environment sensor; s200, preprocessing the acquired data, and performing potential safety hazard identification and environment detection on the construction site; s300, fusing and displaying the generated BIM three-dimensional digital model and the processed data through spatial alignment, data mapping and dynamic updating; s400, calculating the probability of occurrence of potential safety hazards on the construction site and a comprehensive decision score; and S500, carrying out quantitative assessment on the potential safety hazards of the construction site by constructing a risk assessment model, sorting the calculated risk scores of the potential safety hazards, and determining the priority processing sequence of the potential safety hazards of the construction site, so that intelligent safety management of the construction site is realized.

Aspects of the subject disclosure relate to a vehicle camera system. A device implementing the subject technology includes a first set of cameras and a processor configured to receive first data from at least one camera of a second set of cameras of an object configured to be towed by a vehicle and second data from at least one camera of the first set of cameras. The processor may determine, using a trained machine learning algorithm, a set of sub-pixel shift values that represent relative positions of images in the first data and the second data based on a position of the at least one camera of the first set of cameras. The processor may align, using the trained machine learning algorithm, the images based on the set of sub-pixel shift values, and combine the aligned images to produce a stitched image having a combined field of view.

A vehicular driver monitoring system includes an interior rearview mirror assembly including a mirror head that accommodates a mirror reflective element, a video display screen, and a driver monitoring camera that views at least a driver's head region of the cabin of the vehicle. The vehicular driver monitoring system, based at least in part on processing of image data captured by the driver monitoring camera, determines a gaze direction of the driver. Based at least in part on determination that the gaze direction of the driver is directed is toward the mirror assembly, the video display screen is electrically operated to display video images at a first brightness, and responsive to determination that the gaze direction of the driver is not directed toward the mirror assembly, the video display screen is electrically operated to display video images at a second brightness that is less than the first brightness.

A system accesses, during a camera scan of a 3D physical object, video feed data of a user computing device including a plurality of frames. The system generates camera scan data including a set of 2D images of the physical object generated from a subset of the plurality of frames. Generating the camera scan data can include, responsive to determining that a translation between a previous frame and a current frame is greater than a threshold, including the current frame in the camera scan data. Generating the camera scan data can include excluding the current frame from the camera scan data responsive to determining an undesired camera movement type associated with the current frame. Generating the camera scan data can include indicating, in a surface coverage preview model, points of the 3D physical object included in at least a predefined number of consecutive frames of the subset of frames.

A method to employ a three-dimensional (3D) model is provided. The method may include: displaying, on a graphical user interface (GUI) of a display device, at least one selectable option corresponding to at least one 3D model stored in a storage, each of the at least one 3D model corresponding to a 3D data representation and a location tracking data representation; receiving a selection of an option from a user via the GUI, the selected option corresponding to a selected 3D model of a reference surface; in response to receipt of the selection of the option, switching to display a real-time camera view on the GUI; determining that the reference surface is present in the real-time camera view; and superimposing at least one of the 3D data representation or the location tracking data representation of the selected 3D model on the reference surface displayed in the real-time camera view.

A video perspective method based on virtual reality relates to the technical field of virtual reality, and comprises: at least two cameras for capturing video information in real time, the cameras being provided with spatial positions and angles; performing geometric correction on the video information; the eye movement tracking module monitors the gazing direction of the eyes of the user in real time and adjusts the display position; the eye movement tracking module tracks eyeball movement; reconstructing a three-dimensional model of the surrounding environment of the user; based on the three-dimensional model, determining a relative position relationship between the virtual object and the real object; the head posture sensor obtains head movement information of a user; performing semantic segmentation on the video frame through a deep learning algorithm; and synthesizing the video information and the virtual content on the head-mounted display device, and outputting the synthesized video information and virtual content. The technical problems of visual discomfort and dizziness caused by position mismatching can be solved, and natural fusion of a real scene and a virtual scene is realized.

In one embodiment, a method includes initializing latent codes respectively associated with times associated with frames in a training video of a scene captured by a camera. For each of the frames, a system (1) generates rendered pixel values for a set of pixels in the frame by querying NeRF using the latent code associated with the frame, a camera viewpoint associated with the frame, and ray directions associated with the set of pixels, and (2) updates the latent code associated with the frame and the NeRF based on comparisons between the rendered pixel values and original pixel values for the set of pixels. Once trained, the system renders output frames for an output video of the scene, wherein each output frame is rendered by querying the updated NeRF using one of the updated latent codes corresponding to a desired time associated with the output frame.

The invention discloses a three-dimensional attitude high-precision acquisition and reconstruction system for exercise training, which relates to the technical field of three-dimensional attitude acquisition and reconstruction and comprises a camera calibration module, an image acquisition module, a key point extraction module, a three-dimensional reconstruction module, an attitude optimization module, a data fusion module and a feedback generation module. The camera calibration module is used for performing internal and external parameter calibration on a multi-view camera assembly by adopting a self-adaptive joint calibration method to obtain a projection matrix parameter and a transformation matrix of each camera; the image acquisition module is used for synchronously acquiring multi-view video images of the athlete in the training process based on the calibration parameters, and performing timestamp alignment processing to obtain an original image sequence; and the key point extraction module is used for detecting and identifying a human body image by adopting a deep learning model, extracting two-dimensional coordinates of key human body joint points of each frame from an original image sequence, and obtaining a two-dimensional key point set under each view angle.

The invention relates to a cloth color difference intelligent detection method based on an improved vision algorithm, and the method comprises the following steps: S1, obtaining the spectral image data of cloth, and carrying out the calibration; s2, setting of a high-definition camera is adjusted according to the real-time collected image features, and high-definition image data of the cloth is obtained based on the high-definition camera; s3, preprocessing the high-definition image data; s4, based on the preprocessed high-definition image data, performing cloth and background segmentation by using a deep network EfficentNet in combination with an Attention mechanism, extracting features, and establishing a comprehensive feature vector; s5, constructing a color difference detection model based on the improved SVM, and carrying out color difference detection on the cloth image based on the comprehensive feature vector; and S6, combining a fuzzy logic algorithm, and diagnosing the abnormal color difference condition of the cloth according to the color difference result. The cloth color difference detection accuracy and reliability are effectively improved.

A hydraulic engineering video monitoring intelligent stationing optimization design method based on a three-dimensional model realizes video monitoring intelligent stationing planning design based on a mode of combined development of a three-dimensional modeling platform and a BIM lightweight engine. The method comprises five stages of camera adaptive arrangement, monitoring area three-dimensional visualization, monitoring coverage check, design optimization and design result display output. According to the method, the overall planning of the video monitoring intelligent stationing of the water conservancy and hydropower engineering is enhanced, the advantages and disadvantages of the video monitoring stationing are scientifically and quantitatively evaluated, and a quantifiable video monitoring intelligent stationing planning scheme is provided. According to the method, the requirements of the current video monitoring distribution point and the monitoring coverage rate are combined, a monitoring model optimization algorithm is utilized, the monitoring coverage rate or the camera configuration is taken as an optimization target, the monitoring coverage rate is improved, the monitoring blind area range is reduced, monitoring redundancy setting is avoided, and the monitoring camera configuration is optimized.

Video inspection systems are disclosed. In one embodiment a video camera for use in a pipe inspection system includes a camera head including an outer housing, a camera module assembly including video and non-video sensors, a processing circuit, and a data link receiver.

According to the bridge crane intelligent safety monitoring method and device based on multi-source data fusion, by integrating the lateral pressure type pressure sensor, the intelligent camera, the displacement sensor and the electromagnetic sensor, multi-dimensional collection of weight, image, position and broken wire data is achieved. A multi-source data stream is processed by adopting frequency division sampling and a time synchronization mechanism, and accurate detection of personnel in a dangerous area is realized by combining a convolutional neural network configured by a self-adaptive residual block. The system monitors multi-dimensional safety risks such as weight overrun of a hoisted object, breakage of a steel wire rope and deviation of a lifting hook track at the same time, and all-dimensional intelligent monitoring of the operation process of the crane is achieved. The problem that a traditional single monitoring scheme is insufficient in perception capability is effectively solved, and the accuracy of safety early warning is improved.

A hybrid display system for a force measurement assembly, an exercise device, or an interactive game is disclosed herein. The hybrid display system includes a head-mounted visual display device having an output screen; at least one camera or a substantially transparent lens for enabling a system user to see one or more images of a real-world environment outside of the head-mounted visual display device; and a data processing device configured to generate a virtual reality environment and display the virtual reality environment on the output screen of the head-mounted visual display device, and the one or more images of the real-world environment being visible through the substantially transparent lens or the data processing device further configured to project at least a portion of the one or more images of the real-world environment captured by the at least one camera into the display of the virtual reality environment.