5870 results about "Video camera" patented technology

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 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 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.

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.

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.

Disclosed is a image processing apparatus and a method for controlling the image processing apparatus. The image processing apparatus according to an embodiment of the present disclosure may identify an object from an acquired image, determine whether the object is hidden by another object by using an aspect ratio of a bounding box of the detected object, and based on the object being hidden, estimate an entire length of the object based on coordinate information of the bounding box. Accordingly, the size information of the hidden object may be efficiently identified while a large amount of database is applied or resources of the apparatus is minimized. The present disclosure may be in connection with a surveillance camera, an automotive driving vehicle, an artificial intelligence module of at least one of a user terminal or a server, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

The invention provides an in-tunnel inrush water pressure monitoring method and system based on dual-acquisition multi-modal data, and belongs to the technical field of inrush water pressure monitoring. Comprising the steps of collecting video data by adopting a high-definition camera; sequentially performing water flow area intelligent segmentation, optical flow calculation and ConvLSTM time sequence prediction on the video data to predict the water flow velocity; an infrared camera is adopted to collect temperature data; self-adaptive temperature gradient calculation and temperature anomaly detection are sequentially carried out on the temperature data to predict the water flow temperature; aligning and fusing the predicted data of the water flow velocity and the water flow temperature, and constructing a water pressure calculation model to obtain a water pressure predicted value; and determining an early warning level according to the water pressure predicted value so as to adjust the monitoring mode of the inrush water pressure in the tunnel. According to the method, the infrared technology and the vision field technology are combined, accurate prediction of the inrush water pressure in the tunnel can be achieved, and a corresponding early warning monitoring mode is provided according to the prediction result.

The invention provides a highway situation awareness method and system, and the method comprises the following steps: collecting camera video stream data to extract traffic flow data and parking data, and inputting the traffic flow data into a graph neural network to construct a traffic flow propagation model; constructing an abnormal event influence evaluation model based on the recurrent neural network and the long-short-term memory network; and through an abnormal event influence evaluation model, outputting influence range data including an affected road segment set and predicted abnormal recovery time, integrating the data and outputting the data to a visual interface. According to the method, the traffic flow state of the expressway is accurately evaluated by collecting, processing and analyzing the video stream data of the roadside camera, and a reliable situation awareness model is constructed in combination with toll station entrance and exit data, portal snapshot data and the like, so that real-time and accurate monitoring and prediction of the traffic condition of the expressway are realized, powerful decision support is provided for traffic management, and the traffic flow state of the expressway is accurately evaluated. And the operation efficiency of the expressway is improved.

The invention relates to a data fusion method based on a multi-channel image acquisition card and related equipment, and the method comprises the following steps: synchronously receiving a multi-view target scene shot by a camera through the multi-channel image acquisition card, and obtaining original multi-channel image data; performing geometric correction on the original multi-channel image data to obtain a geometric correction image group, and performing space-time coordinate conversion based on the geometric correction image group to obtain a space-time registration image group; performing cross-scale feature aggregation on the space-time registration image group to obtain a multi-scale feature pyramid layer, and constructing a multi-scale feature pyramid based on the multi-scale feature pyramid layer; and carrying out adaptive weight convolution fusion on the multi-scale feature pyramid to generate a target fusion image, thereby solving the technical problems that most methods depend on a complex preprocessing process or have relatively strong hypothesis for a specific scene and are difficult to adapt to a dynamically changing real environment.

A method for generating broadcasts including receiving game state data and user data of players participating in a gaming session of a video game. A spectator zone-of-interest in the gaming session is identified having a scene of a virtual gaming world that is viewable from camera perspectives in the virtual gaming world. Statistics and facts are generated for the gaming session based on the game state data and the user data using a first AI model trained to isolate game state data and user data that are of interest by spectators. Narration is generated for the scene using a second AI model configured to select statistics and facts from the statistics and facts generated using the first AI model, the selected statistics and facts having a highest potential spectator interest as determined by the second AI model configured to generate the narration using the selected statistics and facts.

The invention discloses a multi-view construction personnel tracking method and system based on attention perception. The method comprises the following steps: giving synchronous images from S cameras, and inputting the synchronous images into an encoder for feature extraction to obtain a multi-view feature map; transforming the multi-view feature map into a unified aerial view space by using perspective projection, and aggregating features after projection transformation of all views by using a convolutional layer; inputting the aggregated aerial view features into a decoder for decoding; a cross attention module is introduced, bird's-eye view features of a current frame and an adjacent frame are processed through 3D position coding, instance tokens are extracted as query, keys and values, an affinity matrix is generated through CNN coding similarity, features are propagated through matrix multiplication, and the bird's-eye view features of the current frame are updated. According to the method, the multi-view feature map is projected to the aerial view to realize early fusion, and a cross-frame attention mechanism is introduced, so that the problem of appearance feature distortion caused by perspective transformation is solved.

A vehicular driver monitoring system includes a vehicular interior rearview mirror assembly having a mirror head that accommodates a mirror reflective element. A video display is disposed behind the mirror reflective element and operable to display video images captured by a rearward viewing camera of the vehicle. A driver monitoring camera and a near infrared light emitter are accommodated by and move in tandem with the mirror head. The near infrared light emitter is accommodated within the mirror head so that, with the mirror head adjusted relative to the mounting base to set the rearward view of the driver of the vehicle, a beam of near infrared light emitted by the near infrared light emitter is directed toward a driver's region of the vehicle. The driver monitoring camera is disposed adjacent to the video display screen so as to not view through the video display screen.

An apparatus comprising an infrared (IR) illuminator, an interface and a processor. The IR illuminator may comprise an array of emitters divided into a plurality of segments each configured to generate an amount of an IR light. The interface may be configured to receive pixel data comprising the IR light. The processor may be configured to process the pixel data arranged as video frames, extract IR illumination data for each of a plurality of zones of the video frames, perform a comparison of the IR illumination data in each zone to an exposure threshold, and generate a control signal in response to the comparison. Each zone may correspond to a subsection of the video frames associated with a respective one of the segments. The control signal may be configured to independently adjust the amount of the IR light generated by each of the segments for each of the zones.

The invention discloses an AI video output system and method combined with a text image model, and relates to the technical field of AI video output, and the system comprises a visual angle conversion and multi-visual angle generation module which comprises a visual angle encoder and a multi-visual angle generator based on semantic-style hidden representation and visual angle vectors, the method is used for generating a multi-angle illustration sequence aiming at different camera parameters and keeping structure and style consistency among different visual angles through time domain consistency constraint in the generation process. According to the method, the artistic style is propagated and maintained among multiple frames and multiple visual angles according to a three-dimensional visual angle transformation rule through visual angle perception style propagation, and the generator outputs a consistent multi-angle illustration sequence under the constraint of a visual angle condition during sampling, so that the problem of inconsistent style drift and deformation under multiple visual angles or multiple lenses is solved; the incoherence of stroke, texture and main body structure caused by visual angle change is avoided, the later manual correction is obviously reduced, and the content consistency and the impression professional degree are improved.

The invention provides an indoor SLAM map construction method and system, and relates to the technical field of image processing, and the construction method comprises the steps: building a unified global space reference through cross-device space-time calibration, supplementing the visual blind area information of a robot in real time through the global static features extracted by an environment camera, and obtaining the visual blind area information of the robot; and meanwhile, dynamic interference features are accurately eliminated in combination with continuous frame analysis, and finally, the pose of the robot is continuously corrected by taking global features as anchor point constraints in back-end optimization. The complete technology chain enables the constructed indoor three-dimensional map to have higher integrity, precision and consistency, significantly improves the positioning robustness and navigation reliability of the robot in an environment with dense goods shelves and dynamic activities, reduces the performance dependence on a robot body sensor and the scene reconstruction cost, and improves the reliability of the robot. And a stable and efficient environment sensing basis is provided for automatic operation of industries such as storage and archive management.

The invention discloses an intelligent video monitoring system and method supporting AI strategy cloud side end cooperative execution. According to the system, unified management of computing power resources and dynamic deployment of an AI algorithm are realized based on an extended standardized protocol through a three-level architecture of a cloud side center platform, an edge computing node and an end side camera. The method comprises the following steps: a cloud platform acquires computing power information of edge equipment; an AI algorithm execution strategy is generated and issued according to service requirements; if the required algorithm is not deployed in the equipment, the equipment is controlled to download and automatically deploy an algorithm module; and finally, the driving equipment executes AI analysis and recovers the structured data. According to the invention, AI computing tasks are reasonably distributed through a cloud side-end cooperative computing architecture, so that the computing power construction cost of a central platform is effectively reduced; through algorithm and hardware decoupling and a standardized protocol interface, ecological binding is broken, on-demand loading and flexible scheduling of the algorithm are realized, and the economical efficiency, the flexibility and the intelligent level of the system in large-scale scenes such as smart cities and the like are remarkably improved.

The invention relates to an intelligent oil containment boom mechanical arm device and a control method thereof. The intelligent oil containment boom mechanical arm device comprises a mechanical arm, a hydraulic driving system, a sensing system and a control unit. The mechanical arm comprises a transmission base, a steering stand column, a large arm, a small arm, an overturning head and a tail end grabbing hook assembly. The hydraulic driving system comprises a hydraulic pump station, a proportional reversing electromagnetic valve group and a plurality of execution oil cylinders; the sensing system comprises a gyroscope array and a camera array to form a distributed attitude sensing network and a distributed visual sensing network; the control unit identifies and positions an oil containment boom target based on a sea surface image acquired by the distributed visual perception network, performs motion planning on the mechanical arm, and controls the mechanical arm to move and grab the target; in the target positioning process, posture changes of all parts of the mechanical arm are sensed in real time based on the distributed posture sensing network, and dynamic compensation is conducted on the posture error of the mechanical arm. According to the device, the oil containment boom can be quickly, accurately, reliably and intelligently grabbed, and the laying efficiency and safety of the oil containment boom are improved.

The invention relates to the technical field of image processing, and discloses a traffic behavior real-time identification method based on multi-modal spatial-temporal feature fusion. Traffic scene videos, images and traffic signal lamp state information are collected through a road monitoring camera, and target detection and tracking are carried out on video frames to obtain a target spatial-temporal trajectory; respectively extracting a target visual feature sequence and a signal feature sequence after signal lamp state coding by using a time sequence-space perception module, inputting the two sequences into a multi-modal semantic coupling and fusion network, and generating a fusion feature vector through feature alignment; and based on the vector, target behaviors are discriminated in real time through a behavior classifier, and a normal or abnormal detection result is output and an alarm is given. According to the method, multi-modal information is fused, semantic understanding and dynamic expression are enhanced, recognition robustness is improved, real-time performance and flexibility are achieved, misjudgment and missed judgment can be effectively reduced, and the method has great significance in improvement of the efficiency and the safety level of an intelligent traffic system.

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.

A method and system for estimating dimensions of vehicle exterior defects using multiple cameras arranged in a predefined configuration. The method comprises receiving images from multiple strategically positioned image sensors including side cameras parallel to a vehicle height axis, diagonal cameras at an inclined angle, and roof top cameras perpendicular to the height axis. An angle to detected defects is computed based on image sensor parameters. Different distance calculations are applied based on vehicle section location, with specialized formulas for windshield, back window, and roof components. Defect sizes are computed by determining multiple defect dimensions, with at least one dimension being adjusted by an angular correction factor derived from the relationship between camera angle and vehicle surface orientation. The system implements comprehensive validation through cross-referencing between cameras, comparison with known specifications, and measurement consistency analysis across multiple frames.

The invention relates to the technical field of traffic management, in particular to an urban road intersection passage intelligent optimization method based on multi-modal information, which comprises the following steps: acquiring real-time sensing data of pedestrians and non-motor vehicles through a video camera, a millimeter wave radar and a laser radar, and generating fusion sensing data through timestamp synchronization and coordinate system unification; identifying and generating a target list with category labels by using a detection and clustering algorithm, and obtaining a stable motion trail and intensity by combining with multi-target tracking; predicting a crossing intention and a path based on time sequence deep learning, calculating an interleaving point and quantifying a conflict risk; and according to a comparison result of the conflict risk coefficient and a threshold value, generating a strategy control instruction of different time periods, different paths or a mixed mode, and in combination with execution time window information, forming an optimized timing scheme through cooperative execution of an intelligent prompt identifier, a telescopic isolation belt and a signal control machine, so as to realize cooperative passage. The method improves the recognition precision, reduces the conflict risk, and improves the passing efficiency.

Disclosed are a method, system, and apparatus of a body-worn camera system with integrated artificial intelligence for real-time field assistance and automated incident reporting. In one embodiment, a body-worn safety device includes a body-worn camera configured to capture a video of an incident from a perspective of a wearer of the body-worn camera. In this embodiment, a microphone is configured to capture audio concurrently with the video. A processing unit includes an artificial intelligence module in this embodiment. The artificial intelligence module is configured to respond to a voice command from the wearer by analyzing the captured audio, the captured video, and / or an external data of the artificial intelligence module. In another embodiment, a method of a wearable safety system provides an audible answer and a guidance in real-time using the natural language queries; and outputting an audio response and an alert to the wearer.

The invention provides a multi-head gun-type camera multi-angle intelligent tracking method and system, and the method comprises the steps: obtaining image flow data through a multi-head gun-type camera, building a D-NeRF model through combining with an improved multi-layer perception mechanism, outputting a virtual viewpoint image, a scene depth image, a shielding relation image and a motion vector field according to the D-NeRF model, obtaining a volume density field, and carrying out the multi-head gun-type camera multi-angle intelligent tracking. Analyzing the volume density field, acquiring a continuous aggregation area and performing cutting operation, generating a target instance, acquiring a bounding box and a centroid coordinate in combination with a scene depth map, acquiring appearance characteristics and morphological characteristics and performing matching, generating an identity recognition result, performing trajectory prediction through physical constraint, generating a probability space-time trajectory and performing evaluation, and performing identification on the probability space-time trajectory. According to the method, the uncertain area is obtained, the motion of the camera is subjected to joint optimization in combination with the probability space-time trajectory, the intelligent control instruction is generated, and multi-angle intelligent tracking is performed according to the intelligent control instruction, so that the intelligent level of multi-camera monitoring and tracking in a complex scene is improved.

The invention discloses a material movement behavior monitoring method and system based on monocular vision, and relates to the field of material movement monitoring, and the method comprises the steps: firstly collecting a material movement video through a monocular high-speed camera, and extracting a continuous state sequence of a material on a two-dimensional image plane through employing an instance segmentation and time sequence tracking technology; the method is characterized in that a time sequence deep learning model is introduced to analyze a state sequence, and a key event that a material collides with a working surface of equipment is automatically identified, so that the collision moment and contact point coordinates are accurately obtained. In this way, the collision contact point is used as a geometric anchor point for three-dimensional space back calculation, a two-dimensional image track is reversely projected and reconstructed into a three-dimensional space motion track in combination with internal parameters of a camera and physical parameters of an equipment reference plane, then key kinetic parameters such as the speed and the recovery coefficient are calculated, and low-cost and high-precision automatic online monitoring is achieved.

Smart dental treatment chair, and a system utilizing Artificial Intelligence (AI) and computerized vision analysis to dynamically monitor real-time progress of a dental treatment, to dynamically report the progress to the patient, and to provide additional benefits to dental patients. A dental treatment chair includes video cameras that capture real time video, and a microphone that captures sound and speech. Computerized vision unit perform analysis of the video, and a Large Language Model (LLM) performs analysis of text extracted from uttered speech, to determine the current step in a multiple-step dental procedure. A display unit is oriented towards the patient, and displays a dynamically-updated progress bar and percentage value, indicating the actual progress of the ongoing dental treatment. Optionally, the smart dental treatment chair also integrally plays music that the patient selects and controls, sprays an aromatic agent, and provides other benefits to the dental patient.

An apparatus and a method of controlling a camera is provided. The apparatus includes a receiver configured to receive an image from an image sensor and a processor configured to detect a target object from the image, perform a first zoom adjustment operation of the camera including the image sensor based on whether the target object is changed and perform a second zoom adjustment of the camera based on a center point of the target object and a center point of the camera.

There is provided an information processing apparatus to make it possible to confirm color tone and the like of a captured video at a time point before capturing a virtual video on a display by a camera. The information processing apparatus includes: a rendering unit (520a) that generates a virtual video by performing rendering using a 3D model used in an imaging system that captures, by a camera (502), a video on a display (505) that displays the virtual video obtained by the rendering using the 3D model; and a video processing unit (33) that performs, on the virtual video generated by the rendering unit, actual-imaging video conversion processing for generating a simulation video by using a processing parameter that achieves a characteristic of luminance or color at a time of imaging by the camera used in the imaging system.

The invention relates to a transformer substation patrol preset position correction method and system based on deep learning, and the method comprises the steps: obtaining a template image, obtaining a real-time to-be-detected image, carrying out the down-sampling of the real-time to-be-detected image, obtaining a down-sampling to-be-detected image, and carrying out the preprocessing of the obtained image; performing feature point extraction and sparse feature sampling on the preprocessed down-sampling to-be-detected image to obtain an initial feature point set, and performing lightweight matching on the initial feature point set to generate an initial matching pair; performing feature point extraction and sparse feature sampling on the extracted refined region according to initial matching to obtain refined features, and obtaining a matching result set based on a deformable attention mechanism; constructing a homography matrix based on the matching result set, and calculating the compensation displacement of the camera; and generating a correction instruction according to the compensation displacement, and adjusting the angle of the holder according to the correction instruction to realize position updating of the preset position. According to the invention, the time consumption for deviation correction can be greatly reduced, and the robustness in strong light and shadow scenes is obviously superior to that in the prior art.

A system for multitask detection performs subject tracking by processing image frames from one or more video cameras deployed in a monitored environment. The system uses a neural network to detect human subjects in each frame and extracts feature sets for each subject. These features include a semantic center of the body and directional vectors extending to other body parts, such as the head or face, forming a subject-specific fingerprint. The system compares these fingerprints across frames to identify instances of the same subject over time. By correlating subject positions in image frames with the geolocation data of the capturing cameras, the system computes global coordinates for each subject. Using both the subject-specific fingerprints and spatial coordinates, the system determines trajectories of individuals, including transitions between camera views.