10198 results about "Video camera" patented technology

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.

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.

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.

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.

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.

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.

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.

The invention provides an artificial intelligence-based optical coating quality monitoring method, which comprises the following steps of: 1, acquiring visual image data of the surface of a coating substrate in real time by using a common industrial camera in an optical coating process; 2, preprocessing the collected image data, and extracting visual feature information representing the state of the film layer; 3, inputting the extracted feature information into a pre-trained artificial intelligence analysis model for simulating artificial experience judgment; step 4, outputting an evaluation result of the current coating quality by the artificial intelligence analysis model, wherein the evaluation result comprises identification of film layer uniformity, thickness trend and potential defects; and 5, generating a process adjusting instruction according to the evaluation result, feeding back the process adjusting instruction to a control system of the coating equipment, and dynamically adjusting process parameters to realize closed-loop optimization control.

The invention discloses a method for determining a preset position of a video monitoring camera. Camera presetting positions usually need to be manually set one by one, and time and labor are wasted. The method is used for a video monitoring system, and automatic setting of the preset position of the camera is realized by utilizing a group of scene images and the to-be-monitored target image, so that the efficiency is greatly improved. The method comprises the following steps: acquiring feature points based on a scene image set by adopting a computer vision technology, generating a three-dimensional model of a scene by adopting a motion recovery structure technology and a surface reconstruction algorithm, and obtaining the position of a to-be-monitored target in a three-dimensional space by matching the feature points of a to-be-monitored target image with the feature points of the scene image set. Therefore, a proper camera is selected, a corresponding preset position is calculated, and finally the preset position is corrected according to an actual monitoring effect.

Methods and processors for rendering a 3D object are disclosed. The method includes acquiring multi-camera image input including first image frames of the 3D object generated by a first camera and second image frames of the 3D object generated by a second camera, acquiring an initial 3D Gaussian Splatting (3DGS) model having a plurality of initial parameters including an initial frame-wise GS parameter and an initial camera-wise GS parameter, generating an adjusted 3DGS model by adjusting, based on the multi-camera image input, at least one of: the initial frame-wise GS parameter, the initial camera-wise GS parameter, generating, by the adjusted 3DGS model, a 3DGS output and rendering a 2D image of the 3D object using the 3DGS output.

An electronic door system includes a door frame, a door hingedly coupled to the door frame, an electronic locking mechanism coupled to the door, one or more lights coupled to the door frame, a camera system including at least two of a camera, a proximity or motion sensor, or a doorbell, and a wiring harness extending through the door frame and the door. The wiring harness is coupled to the electronic locking mechanism, the one or more lights, and the camera system. The wiring harness is configured to electrically couple to an external power source.

An electronic device includes: a projector; a camera; one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the electronic device to: control the projector to display a projected image on a screen, wherein the screen includes surfaces; control the camera to obtain a captured image including the surfaces and the projected image; identify, based on the captured image, shapes of the surfaces and areas of the projected image projected onto the surfaces; and correct the projected image, based on the shapes of the surfaces and the areas of the projected image, by transforming the projected image into one from among a three-dimensional image and a flat image.

Systems and techniques are provided for calibrating cameras in a real space for tracking puts and takes of items by subjects. The method includes first processing one or more selected images captured by an extrinsic calibration tool to extract from the images, a three-dimensional (3D) point cloud of points corresponding to displays or structures that remain substantially immobile in the real space, and a second processing of a second set of images captured by a camera installation in the real space to match points in the second set of images to the point cloud. Differences in position of matching points can be used to determine transformation information therebetween, which can be used to calibrate the installed cameras.

The invention relates to the technical field of PIV (particle image velocimetry) flow field testing devices, in particular to an internal peeping type PIV testing device and method for an internal flow field of turbomachinery, and the device comprises the turbomachinery, a testing assembly and a connecting assembly. The testing assembly comprises a lighting mechanism, a camera shooting mechanism, a laser mounting pipe arranged on the air inlet pipe, a mounting base arranged on the laser mounting pipe and a camera shooting mounting mechanism arranged on the air inlet pipe, and the connecting assembly comprises an inserting mechanism arranged on the laser mounting pipe and a connecting mechanism arranged on the camera shooting mechanism. Through the arrangement of the mounting base and the camera mounting mechanism, a laser light source emitter and a camera are quickly mounted, the camera can adapt to various special-shaped shells such as volutes, the angle of a sheet light source emitted by the laser light source emitter is quickly adjusted through the arrangement of a rotating pipe, and the angle of the sheet light source is adjusted through the arrangement of a connecting mechanism and an inserting mechanism. And the sheet light source emitted by the laser light source emitter is quickly perpendicular to the shooting direction of the camera mechanism again.

The invention provides a multi-small-target tracker and tracking method based on SAM2, and the method comprises the steps: dividing a video into a plurality of segments, and enabling the last frame of a previous video segment to be overlapped with the first frame of a next video segment; performing target detection on a first frame of the initial video clip, and allocating an ID to a detected target object; target tracking is executed in each video clip through SAM2, target detection is executed on overlapped frames between adjacent video clips, a detected bounding box is matched with a mask output by SMA2 through a previous video clip according to a mask-to-detection association strategy, and target tracking is executed in each video clip through SAM2; therefore, when a new target appears in the next video clip, a new ID can be allocated to avoid tracking interruption. According to the method, the mask and the detection are located at the same space-time position through video frame overlapping, so that tracking cannot be interrupted as long as the appearance of the target can still be visually distinguished, and the tracking failure rate when the size of the target is too small or the camera is zoomed and moved can be remarkably reduced.

The invention discloses an intelligent film and television scene synthesis method based on generative multi-mode script semantic mapping. The method comprises the following steps: firstly, performing semantic unit segmentation and dependency structure analysis on a movie and television play text, and establishing a script semantic mapping relationship; carrying out cross-modal feature mapping training by adopting a generative multi-modal semantic alignment method so as to generate an initial scene layout map; performing space-time consistency optimization on the initial scene layout map, and constraining a synchronization relation between a role action sequence and a camera visual angle according to a time sequence and space depth information; refining and reconstructing the scene layout map sequence to generate a rendering result sequence; and determining a lens switching rhythm and a focal length change track according to a rendering result sequence and a script semantic mapping relationship, and automatically generating a playable movie and television scene video with natural plot logic and uniform visual style. According to the method, the generative semantic self-adaptive synthesis from the script text to the video content is realized, and the automation and intelligence level of film and television production is improved.

The invention provides a network camera monitoring identification method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a monitoring data stream which is outputted by a network camera and comprises a video frame sequence and a corresponding time sequence metadata sequence, and then carrying out the spatial-temporal context coding processing of the monitoring data stream; the method comprises the following steps: generating a context feature cube containing spatial position information and time evolution information, then executing a normal behavior mode learning operation based on the context feature cube, and generating a reference feature library containing typical scene feature templates and feature evolution rule description; and dynamically matching and comparing the context feature cube of the current time period with the reference feature library, calculating a feature matching deviation value and generating an abnormal confidence score, and finally generating a monitoring early warning instruction containing abnormal occurrence time, a space coordinate range and a confidence level identifier according to the abnormal confidence score and corresponding space-time position information. And the accuracy and the early warning effect of monitoring and identification of the network camera are effectively improved.

The invention provides a rail turnout foreign matter detection and type analysis method and system, and the method comprises the steps: arranging a first camera group at the front end of a train, recognizing the position of a turnout through a preset target detection model, and calibrating foreign matters in the turnout through a preset foreign matter detection model; shooting an image calibrated to the foreign matter in the turnout through a second camera group as a to-be-analyzed image; extracting a second boundary region rolled by a wheel on the foreign matter from the to-be-analyzed image through a preset wheel rolling track detection model, performing coarse classification on the foreign matter in the to-be-analyzed image to obtain a coarse classification result, and selecting a boundary detection strategy to identify the first boundary region according to the coarse classification result in the foreign matter image; and respectively extracting corresponding texture features in the first boundary region and the second boundary region through a deep learning network, performing one-to-one mapping on the extracted texture features to obtain a mapping relation group, performing analysis through the mapping relation group to obtain a feature change rate, and performing analysis through a multi-feature analysis strategy to obtain a specific foreign matter type.

A personal tactical system including a load-bearing garment, a pouch with one or more batteries enclosed in the pouch, at least one power distribution and data hub, and at least one camera. The camera is incorporated into or removably attachable to the load-bearing garment, the pouch is removably attachable to the load-bearing garment and the one or more batteries are operable to supply power to the at least one power distribution and data hub. The at least one power distribution and data hub is operable to supply power to at least one peripheral device. A plurality of personal tactical systems is operable to form an ad hoc network to share images and other information for determining object direction, location, and movement.

1. The name of the design product: the main body of the connecting handle of digital camera. 2. The use of the design product: the connecting handle of digital camera, which is used when installing various accessories; the use of the partial design is as the main body of the connecting handle of digital camera. 3. The design points of the design product: the shape. 4. The picture or photo showing the design points: the perspective view.

A system for AI-based analysis of student behavior, consisting of: a variety of data collection devices, including cameras, microphones and wearable sensors; a central processing unit (CPU) that is communicatively connected to the data acquisition devices; and a behavioral analysis engine, wherein: (a) the cameras are configured to capture visual data indicating student behavior, including facial expressions, body language and eye movements, with the captured visual data being processed using computer vision techniques to detect certain emotional and engagement-related indicators such as stress, boredom, attention or excitement; (b) the microphones are configured to capture audio data, including speech patterns, tone of voice, volume and level of participation, the captured audio data being processed using natural language processing (NLP) techniques to assess the mood, engagement and verbal participation of students in classroom activities; (c) the wearable sensors are configured to collect biometric data, including heart rate, skin temperature and movement, with the biometric data being analyzed to detect physiological signs of emotional stress, fatigue or engagement; (d) The behavioral analysis engine is configured to process the captured visual, auditory, and biometric data in real time to create a behavioral profile of each student. It uses machine learning models to correlate certain patterns with academic performance, emotional well-being, and social engagement. (e) the system is also configured to create predictive models based on historical data to identify pupils at risk of declining performance, social isolation or emotional distress, and to send alerts to teachers, counselors or the administration to enable timely intervention to address the identified problems.

This item is a digital video camera used for cinema shooting and the like with interchangeable lenses.

Approaches presented herein provide for the automated determination of a level of impairment of a person, as may be relevant to the performance of a task. A light and camera-based system can be used to determine factors such as gaze nystagmus that are indicative of inebriation or impairment. A test system can simulate motion of a light using a determined pattern, and capture image data of at least the eye region of a person attempting to follow the motion. The captured image data can be analyzed using a neural network to infer at least one behavior of the user, and the behavior determination(s) can be used to determine a capacity or level of impairment of a user. An appropriate action can be taken, such as to allow a person with full capacity to operate a vehicle or perform a task, or to block access to such operation or performance if the person is determined to be impaired beyond an allowable amount.

A personal tactical system including a load-bearing garment, a pouch with one or more batteries enclosed in the pouch, at least one power distribution and data hub, and at least one camera. The camera is incorporated into or removably attachable to the load-bearing garment, the pouch is removably attachable to the load-bearing garment and the one or more batteries are operable to supply power to the at least one power distribution and data hub. The at least one power distribution and data hub is operable to supply power to at least one peripheral device. A plurality of personal tactical systems is operable to form an ad hoc network to share images and other information for determining object direction, location, and movement.

The invention discloses a sleep monitoring method and system based on a non-contact video data sequence. The method comprises the steps that S1, video data streams of human body sleep are collected through a camera system; utilizing a YOLOv11 network to obtain face video data and thoracoabdominal video data; s2, the RPPG signal extraction model extracts and obtains an RPPG signal by using the face video data; s3, the breathing signal extraction model extracts thoracic and abdominal micro-motion change characteristics in the thoracic and abdominal video data by using an optical flow method, and noise filtering processing is carried out to obtain thoracic and abdominal motion signals as breathing signals; s4, the oxyhemoglobin saturation extraction model detects and outputs an oxyhemoglobin saturation signal by using the RPPG signal; and S5, performing multi-modal fusion analysis on the multi-physiological index fusion recognition model according to time slice T1 division to obtain a long-time-sequence sleep stage staging result. According to the invention, the physiological index signals are extracted and recognized by adopting the non-contact video data sequence, and the sleep stage staging result with a long time sequence is obtained, so that high-precision sleep monitoring and sleep stage recognition are realized.

In some embodiments, a device mount may include a base, an arm coupled to the base, a connector coupled to the arm with a rotation joint, a first motor disposed within the base and configured to tilt the connector, facilitated by the rotation joint, and a second motor disposed within the base and configured to rotate the base. In some embodiments, a method may include receiving an indication that a device is coupled to a connector of a device mount, receiving a first signal from the device to move the device mount to track a user or an object in view of one or more of the device's cameras, and in response to the first signal, either actuating a first motor to tilt the connector or actuating a second motor to rotate a base of the device mount to track the user or the object.

The invention provides a remote control method and device for a security system, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction of security data of a fire alarm host, and obtaining a fire alarm scene vector; a preset linkage strategy library is matched according to the fire alarm scene vector, and a PTZ positioning instruction is obtained; controlling a camera to collect an alarm image based on the PTZ positioning instruction; based on a preset fire alarm analysis model, generating an alarm confirmation report according to the alarm condition image and the fire alarm scene vector; and notifying a plurality of preset user terminals to perform remote interaction according to the alarm confirmation report, and performing start-stop control on the fire-fighting equipment.