4789 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.

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.

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.

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 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.

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.

The invention discloses a quantitative recognition device for the number and size of bubbles at a rotating impeller of a multiphase pump. The quantitative recognition device comprises a two-phase flow experiment system, the multiphase pump, a high-speed camera, a light source and the like. The preprocessing and data analysis system and the deep learning system are arranged in the computer; the two-phase flow experiment system and the image acquisition system are electrically connected with the computer through lines. The invention further discloses a quantitative recognition method for the number and size of the bubbles at the rotating impeller of the multiphase pump. According to the method, the problems of low blistering recognition precision, low efficiency and large error caused by the existing manual measurement method are solved, and a reference method is provided for exploring the influence of gas distribution at the impeller in the multiphase pump on pump operation and optimizing design of the multiphase pump.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The invention relates to the technical field of production environment monitoring, in particular to a high-temperature and high-brightness production environment monitoring system based on deep learning, which is characterized in that visible light and thermal infrared images in a high-temperature and high-brightness environment are acquired through cooperation of an industrial camera and an infrared camera, space registration and time synchronization are carried out, and accurate alignment of multi-modal data is ensured. Extracting a highlight overflow mask, a dark part texture guide map and color cast estimation, fusing to generate a joint perception feature tensor, inputting the joint perception feature tensor into a model with a cross-modal attention mechanism and time sequence feature modeling, recognizing a potential distortion region in advance, improving the system robustness, outputting an imaging state evaluation result and an imaging parameter reference value, and improving the imaging quality. And furthermore, a camera adjustment parameter is generated through a strategy network, an image acquisition process is dynamically optimized, a prediction control closed loop is constructed, and adaptive maintenance and optimization of image quality in a complex environment are realized.

The invention relates to the technical field of intelligent traffic, and discloses a camera state judgment method based on road surface covering and Hash comparison, and the method sequentially comprises the steps: extracting key frame pairs from a video stream at intervals; performing road surface region extraction on each frame of image to obtain a road surface mask; carrying out covering processing on the road surface area in each frame of image based on the mask to obtain a covered image; calculating a difference hash feature of each frame of covered image to obtain a hash code; calculating the Hamming distance between the Hash codes of the key frame pair; and judging whether the camera is in a rotating or stable state according to a comparison result of the Hamming distance and a threshold value. The method only depends on the video image data, does not need an external sensor, effectively eliminates the dynamic interference of the vehicle through covering the road surface, achieves the efficient and robust judgment of the state of the camera through combining the lightweight difference hash calculation, is low in calculation cost, and is suitable for edge equipment and complex road environments.

The invention provides a vehicle throwing object detection and positioning method and system based on multi-channel spatial-temporal feature fusion, and the method comprises the steps: obtaining a continuous time sequence image frame sequence of a road scene, and generating an optical flow image sequence through an optical flow algorithm; outputting a detection frame of each vehicle in each standardized image through a deep neural network target detection model, and generating a region of interest according to the detection frames; a fusion feature vector is generated for each region of interest, a space-time fusion feature sequence is constructed, and a thrown object classification result and a positioning result of each region of interest are generated based on the space-time fusion feature sequence and the multi-branch full-connection network; and based on the classification result and the positioning result of the thrown object, inputting the obtained coordinates of the suspected area of the thrown object into a spherical camera for tracking. According to the method, the thrown object can be efficiently and accurately detected and positioned automatically, the accuracy and real-time performance of thrown object detection are improved, the false alarm rate is reduced, and the precision degree of positioning the position of the thrown object is improved.

Camera heads configured to provide digitally articulated images or video, at adjustable resolutions and / or offsets and orientations, to a camera control unit (CCU) or other electronic computing system for display, storage, and / or transmission to other systems are disclosed.

The invention relates to the technical field of image data processing, in particular to a workshop filter defect detection method and system based on image processing, and the method comprises the steps: calculating the real-time rotating speed of a fan based on image analysis; dynamically adjusting the frame rate of the camera according to a rotating speed comparison result; reconstructing a three-dimensional surface model of the equipment; performing camera vibration compensation through the coding mark points; fusing the visible light image, the thermal imaging and the three-dimensional model data to generate a comprehensive diagnosis result; compared with the prior art that blade images are blurred and illumination and shadow interference cannot be accurately distinguished when a fixed frame rate camera fluctuates in rotating speed, the method has the advantages that the frame rate is dynamically adjusted by tracking the fan rotating speed deviation in real time, and multiple groups of images of each blade at the same rotating angle position are captured; the blade tip gradient vector is combined to analyze illumination shadow distribution characteristics, real defects and shadow artifacts are effectively separated, the microcrack recognition accuracy is remarkably improved, and meanwhile misjudgment caused by sudden change of environment illumination is avoided.

Embodiments of the present application provide a method, an apparatus, a device, a medium, and a program for generating a cover image of a virtual object. The method includes receiving a generation instruction of a cover image of a virtual object; determining a position of a virtual camera for shooting the virtual object; shooting the virtual object according to the position of the virtual camera; and generating, according to an image of the virtual object shot by the virtual camera, a cover image corresponding to the virtual object. The cover image of the virtual object shot by this method is more complete and aesthetically pleasing.

The present application relates to a kind of heat shock flash evaporation loop lift membrane heat exchange test system, including heat exchange pipe, vapor-liquid separator, condenser, metering barrel, vacuum pump, liquid tank, heat shock heater, throttle valve, measurement and control system and high-speed camera, the upper outlet of heat exchange pipe is connected with the import of vapor-liquid separator, the secondary steam outlet of vapor-liquid separator is connected with the hot medium side import of condenser, the hot medium side outlet of condenser is connected with metering barrel and liquid tank respectively, the concentrated liquid outlet of vapor-liquid separator is connected with liquid tank, the outlet of metering barrel is connected with liquid tank, vacuum pump is connected with liquid tank, liquid tank is connected with heat shock heater, heat shock heater is connected with heat exchange pipe, throttle valve is arranged between heat shock heater and heat exchange pipe;High-speed camera is arranged in one side of heat exchange pipe.The present application can realize the experimental test to heat shock flash evaporation loop lift membrane heat exchange technology, and provide support for mechanism research, equipment optimization of heat shock flash evaporation loop lift membrane heat exchange technology.

This disclosure provides an AI filmmaking workflow including AI-assisted storyboarding, AI animation, and post-production processes for creating films. The workflow provides techniques for reconstructing 3D digital environments and characters, and for virtual camera control. The workflow also provides techniques for capturing 2D live-action performances and extracting visual cues. The AI animation process generates synthetic images and video using prompts that are based on virtual camera control in case of 3D digitization and / or visual cues in case of 2D camera capturing. Further, the workflow provides techniques for compositing with AI assistance, to generate a composited video based on the AI-animated video and inputs resulting from 3D digitization and / or 2D video processing. Advanced post-processing techniques are also provided for generating a complete film based on the composited video. This framework is designed to facilitate creative collaborative networks by using a hybrid digitization approach to enhance consistency, directability, and scalability in AI filmmaking.

The invention relates to a hierarchical occlusion perception grid infinitesimal streaming transmission and adaptive rendering method, which comprises the following steps of: S1, decomposing a model into grid infinitesimal, recursively constructing a bounding volume hierarchical tree BVH from bottom to top by taking the grid infinitesimal as a leaf node, and setting a semantic error and a geometric error for each sub-node cluster; s2, acquiring camera parameters of a current frame, comparing a total screen space error with an error threshold value, if the error is smaller than the threshold value, stopping traversing, and adding the cluster node into a to-be-rendered list of the current frame; and S3, if the grid infinitesimal corresponding to the cluster node in the to-be-rendered list is not loaded to the GPU memory, sending a loading request to an asynchronous streaming manager, asynchronously loading the corresponding grid infinitesimal from the memory, and rendering a father node of the cluster node. Compared with the prior art, the method has the advantages that large-scale and high-precision model data can be rendered on the lightweight XR all-in-one machine in real time, and the like.