57 results about "Aerial video" patented technology

The invention relates to the technical field of aerial video analysis, and discloses a large-scene multi-modal analysis system and method based on an unmanned aerial vehicle. According to the method, an original video frame sequence is obtained from aerial photography equipment, a scene change period and an object motion trend are extracted by calculating inter-frame motion feature vectors, and whether a scene is in a stable or dynamic state is judged by means of a state classification model. Calculating a tracking target position according to preset parameters in a dynamic state, and adjusting and analyzing a target area in combination with a motion trend; generating a camera parameter adjustment instruction according to the deviation value of the target area and the actual object position, and outputting the camera parameter adjustment instruction to an execution mechanism; acquiring a complexity value through the scene complexity feedback signal, updating an analysis strategy by adopting an adaptive algorithm when the complexity value exceeds a preset range, and outputting an adjustment signal; the output time sequence of control and strategy adjustment signals is optimized, meanwhile, the frame sequence is monitored in real time, the parameter change trend is analyzed, the preset parameters are dynamically updated to meet the user requirements, and efficient video analysis processing is achieved.

The invention discloses an unmanned aerial vehicle visual angle multi-target tracking method and system based on improved ByteTrack, and belongs to the technical field of target tracking, and the method comprises the steps: training a target detection algorithm, and carrying out the target detection of a public data set through employing the trained target detection algorithm; setting a high-score confidence threshold and a low-score confidence threshold, classifying detection frames in the target detection result, and dividing the detection frames of the target detection result into high-score detection frames and low-score detection frames; an improved ByteTrack multi-target tracking algorithm is adopted, a shielding matching module is introduced, and target matching is carried out on a high-score detection frame and a low-score detection frame for three times; and updating all successfully matched track frames based on a matching result, reserving a preset frame number of unmatched track frames for subsequent frame matching, and outputting a tracking result of the current frame. The method improves the tracking accuracy and robustness, and is suitable for the multi-target tracking task in the aerial video of the unmanned aerial vehicle.

The invention relates to an unmanned aerial vehicle aerial video vehicle robust tracking method in an anti-shielding scene, and the method comprises the steps: obtaining a road traffic video through the fixed visual angle high-altitude shooting of an unmanned aerial vehicle; marking a specific area in the video frame based on the polygon mask, and generating coordinate points of a polygon area; performing target detection on the video frame based on a pre-trained target detection model, and outputting a vehicle detection frame and category information; through adaptive target tracking, shielding area tracking is carried out on video vehicles in a shielding area, non-shielding area tracking is carried out on video vehicles in a non-shielding area, and a vehicle tracking trajectory with a stable ID and a flow statistical result are obtained; and matching a lane for each video vehicle, and outputting a vehicle tracking trajectory with a stable ID and a traffic statistical result, wherein the vehicle tracking trajectory has a serial number of the lane where the tracked vehicle is located. Compared with the prior art, the problems that the vehicle cannot be continuously tracked and the tracking precision is low due to the shielding object are solved.

The invention discloses a grid-coding-based video space-time retrieval method and device, the grid-coding-based video space-time retrieval method is executed in computing equipment, and the method comprises the following steps: mapping a coordinate range of a target area into a corresponding grid code set according to a Beidou grid standard on the basis of the coordinate range of the target area; acquiring video data with an intersection or inclusion relationship between the spatial range of the shot object and the target area; combining the shooting time of any video data with each grid code to generate a space-time grid code set; constructing an index structure corresponding to the video data by taking each time-space grid code in the time-space grid code set as a main key; and in response to query information of the user, analyzing a space-time range in the query of the user, mapping the space-time range into a corresponding space-time grid code range, executing retrieval based on the range, and returning hit video data. According to the method, rapid positioning and accurate retrieval of large-scale aerial photography video data are realized.

The invention belongs to the technical field of computer vision and digital media, and discloses a dome-screen film production method and system based on a high-altitude aerial image. Projecting the multi-view aerial video sequence to a unified spherical coordinate system, and constructing a panoramic space with an overlapping region; performing non-rigid geometric deformation processing on the overlapping region of the panoramic space to generate an intermediate image with aligned pixels; calculating the fusion cost of each pixel point in the overlapping region in the intermediate image, and performing path search based on the fusion cost of each pixel point to generate a dynamic suture line; generating an image mask based on the dynamic suture line, performing multi-scale frequency domain adaptive fusion on the intermediate image by using the image mask to generate a fused panoramic image, and remapping the fused panoramic image into a projection format adaptive to dome screen playing to output; and the real reducibility and the manufacturing quality of the dome film are greatly improved.

The invention provides an aerial video image target tracking method and system based on broken trajectory repair, and the method comprises the steps: inputting an aerial video image into a sliding window slicing module frame by frame, and segmenting each frame of image into a plurality of image slices P1-Pn; respectively inputting the original images P0 and P1-Pn of each frame into a trained joint detection-tracking model, wherein the model comprises a target detection network and a Re-ID network integrated with a broken trajectory prediction and restoration algorithm; the target detection network carries out target detection on P0-Pn; and the Re-ID network performs target tracking according to the target detection result of each target of each frame: initializing the detection frame coordinate of each target of the first frame into a target trajectory, matching the detection frame coordinate of each target of the current frame with the existing target trajectory by combining the ID identity vector from the second frame, and if the matching succeeds, performing target tracking. And updating the detection frame coordinate of each target of the current frame to the target track, and if the matching is not successful, creating a new target track. According to the invention, target tracking can be carried out on aerial video images.

The present invention relates to a server and method for super-resolution that may vary a super-resolution method for video data (e.g., satellite image, aerial video, drone video, etc.) according to a user's request, and a system including the same. The super-resolution server may include a data collection module that receives video data and requirement information related to the user's needs for the video data, a super-resolution module that super-resolves the video data according to the requirement information to generate a high-resolution satellite image, and an output module that outputs the high-resolution satellite image and reliability information on the high-resolution satellite image.

The invention provides an AR superposition method for a CAD road drawing and an unmanned aerial vehicle aerial video, and the method comprises the following steps: S1, converting the coordinates of the CAD road drawing into latitude and longitude data, and obtaining the elevation information of the drawing; s2, latitude and longitude data and elevation data are extracted from each frame of image of the unmanned aerial vehicle aerial video; s3, establishing connection between the CAD road drawing and the unmanned aerial vehicle aerial video by using the CAD road drawing, the unmanned aerial vehicle aerial video and common known information of the latitudes, longitudes and elevations of the CAD road drawing and the unmanned aerial vehicle aerial video, and realizing projection of the CAD road drawing on the unmanned aerial vehicle aerial video; and S4, finally, all the projected points are connected according to the previous connection mode on the CAD road drawing so as to restore the drawing. According to the AR superposition method of the CAD road drawing and the unmanned aerial vehicle aerial video, rapid and fully-automatic superposition is realized by using information contained in the CAD road drawing and the unmanned aerial vehicle aerial video, so that road construction progress supervision and progress reporting are facilitated.

The invention belongs to the technical field of aerial video editing, and particularly discloses an aerial video AI automatic editing method and system fused with humanity elements, and the method comprises the following steps: receiving aerial materials uploaded by a user, and providing a video template library containing a plurality of scenic spot IPs and humanity elements for the user for selection, wherein each video template is associated with a humanity attribute tag and a technical parameter of a specific scenic spot; based on the video template selected by the user, performing multi-dimensional AI processing on the aerial photography material, including background music matching, subtitle generation, explanation speech synthesis, transition effect addition and aerial photography material embedding; and integrating the processed aerial photography materials according to the sequential logic of the template to generate a complete aerial photography video clip. According to the method, the scenic spot IP and the humanity element are integrated, full-process AI automatic editing of the aerial video can be realized, and the requirements of a user on efficient and high-quality humanity aerial video production are met.

1. Name of the product in this design: Unmanned Aerial Vehicle (UAV). 2. Purpose of this design: To mount a gimbal camera for aerial video or photograph shooting, etc. 3. The key design features of this product are its overall shape. 4. The image or photograph that best illustrates the design's key points: 3D rendering 1.

This invention belongs to the field of UAV autonomous positioning and navigation technology, and relates to a UAV map construction method for environments where satellite imagery is unavailable. Specifically, it provides a method for constructing a UAV work area map based on aerial imagery, addressing issues such as image distortion and edge misalignment in existing area map construction methods. This invention orthogonally divides the UAV's aerial photography work area into a grid according to the same geographic coordinate system. The UAV performs a coverage flight mission within the work area according to a planned flight path, maintaining an interval of 50% of the field of view width between adjacent flight paths. During aerial photography, the UAV simultaneously acquires image and geographic positioning information (GPS information and yaw angle information of the center point) as image location points. Based on UAV sensor data and the UAV's flight trajectory and pose, keyframes of geographic location information are extracted from the aerial video, and then used to fill the corresponding grid content to obtain a global map of the work area.

A display device capable of displaying an aerial video image by utilizing retroreflection is provided. The display device includes a light source including an emission surface for emitting a video image; an optical member configured to separate incident light into reflected light and transmitted light, the optical member being arranged such that a principal surface of the optical member faces the emission surface of the light source in parallel; and a pair of retroreflective members arranged in a region including at least a space between the light source and the optical member. The pair of retroreflective members are arranged so as to be inclined outward with respect to a normal to the emission surface.

The invention discloses a real-time intelligent marking method and system for aerial photography of an unmanned aerial vehicle and a storage medium, and the method comprises the steps: receiving video stream data collected by an airborne camera unit of the unmanned aerial vehicle in real time; performing multi-channel parallel analysis on the video stream, and extracting event features and aesthetic features; performing weighted fusion on the features based on a decision fusion model to generate a highlight confidence score changing along with time; according to dynamic comparison between the confidence score and a preset threshold value, automatically delimiting a highlight video clip; and a structured label based on semantic description is generated for the highlight fragment, and is stored in association with the video in a metadata form. According to the method, a mode of event feature and aesthetic feature conjoint analysis is adopted, real-time highlight fragment identification and intelligent labeling in the aerial photography process of the unmanned aerial vehicle are achieved, the content understanding and later editing efficiency of aerial photography videos is improved, and the problems that in the prior art, highlight fragments depend on manual screening, and the real-time performance and semantic expression ability are insufficient are solved.

The invention provides a construction operation pole falling video detection method and device based on TPF-Net, and belongs to the field of computer vision, and the method is characterized in that the method comprises the steps: carrying out the preprocessing of an obtained construction operation site monitoring video or an unmanned aerial vehicle aerial video, and carrying out the video frame extraction, size normalization, pixel standardization and noise suppression; and the stability of input data is ensured. One branch is a time sequence visual branch, modeling is carried out on a video frame sequence by adopting the time sequence visual feature extraction network, global time sequence features are extracted, and RTFM loss is introduced, so that the sensitivity of a model to abnormal events such as inclination evolution and sudden instability is enhanced; and the other branch is an attitude estimation branch, extracting pole tower key point coordinates by using an attitude key point detection network, calculating a pole body inclination angle and forming a time sequence angle sequence. Dimension raising is carried out on the angle features through a multi-layer perceptron, weighted fusion is carried out on the angle features and the visual features in the same dimension space, unified feature representation is obtained, and finally a classification head is input to complete recognition of a normal state and a pole reversing state. The method is suitable for rapid deployment and control and edge end real-time operation in the construction operation period, detection precision and calculation efficiency are both considered, and missing detection and false detection of electric pole inclination and falling events under complex background, shielding and auxiliary supporting structure interference can be effectively reduced; and important reference is provided for safety management and control, graded early warning and emergency disposal of a construction operation site.

The invention discloses a visual self-supervised learning-based high-altitude operation personnel operation specification detection method. The method comprises the steps of collecting an aerial video of a high-altitude operation area through an unmanned aerial vehicle; pre-training is carried out on the unlabeled data by using a DINO self-supervised model, and high-robustness feature representation is obtained; introducing cavity convolution into the backbone network to enhance a receptive field of small target and fine granularity features; detecting whether the operator wears a safety helmet and a reflective vest based on the enhanced features; judging operation specifications in combination with the time sequence information; and finally outputting alarm information and recording related events. The technical scheme of the invention aims to solve the problems of difficulty in small target detection, low operation behavior recognition accuracy and low manual supervision efficiency in the existing electric power overhead operation inspection.

The application provides a kind of light-weight navigation marker detection method based on KDSR-CNN, comprising the following steps: S1, obtain the aerial video of the unmanned aerial vehicle visual angle in the cruising range;S2, prepare visual navigation dataset and carry out data enhancement;S3, construct light-weight KDSR-CNN network model;S4, knowledge distillation training light-weight network to improve model accuracy;S5, deploy on embedded platform for inference verification.The application significantly reduces the parameter quantity and calculation amount of the model, is more easily deployed on the embedded platform, and at the same time improves the detection accuracy of the navigation marker, so as to effectively improve the positioning accuracy of visual navigation, and effectively realize the real-time aerial target detection of the aircraft.

The invention belongs to the technical field of power transmission and transformation projects, and provides a power transmission and transformation project live-action video generation method and system fused with multi-modal data, and the technical scheme comprises the steps: obtaining aerial video data of a power transmission and transformation project site; constructing a power transmission and transformation project power grid information model; performing lightweight processing on the power transmission and transformation project power grid information model to obtain a lightweight power transmission and transformation project power grid information model; carrying out conversion processing on aerial video data of a power transmission and transformation project site, and matching the aerial video data with the lightweight power transmission and transformation project power grid information model in a spatial dimension; extracting continuous power transmission corridor trajectory features based on the aerial video data after space matching; performing feature matching and pose solving based on the extracted continuous power transmission corridor trajectory features, and fusing the aerial video and a GIM model in a spatial dimension based on feature matching and pose solving results to obtain a fusion result; and generating a live-action synthetic video based on a fusion result of the aerial video and the GIM model.

The invention discloses an aerial video real-time labeling method and device. The method comprises the steps that an unmanned aerial vehicle fence is obtained according to the position of the unmanned aerial vehicle after the unmanned aerial vehicle takes off, the unmanned aerial vehicle fence is stored in a fence library, the range of the unmanned aerial vehicle fence is a circular area with the longitude and latitude point of the unmanned aerial vehicle as the center, and the radius of the circular area is a real number larger than 0; all point locations in a camera coordinate system of the unmanned aerial vehicle are obtained, the point locations meeting the view cone equation are reserved, and the point locations not meeting the view cone equation are removed; and aggregating the point locations according to the height of the unmanned aerial vehicle. According to the invention, the problems of low efficiency, resource waste and inaccurate scattering point when the unmanned aerial vehicle loads the scattering point information in real time in the prior art are solved.

A method (1300) and system (201) for generating an astro-lapse video on a user device is disclosed. Initially, at least one current frame comprising one or more stars, is captured via the user device. Further, region(s) with a degree of exposure and focus greater or less than a predefined threshold are determined and a set of subsequent frames including the stars are captured. The exposure and focus of region(s) in the subsequent frames is corrected during the capturing based on degree of exposure and focus of the region(s) within the current frame. Further, fusion weights of the current and subsequent frames are determined based on one or more cloud categories and associated cloud density scores. Finally, an astro-lapse video is generated by fusing the at least one current frame and the set of subsequent frames based on the determined fusion weights.

This invention provides an intelligent monitoring and boundary violation early warning method for construction planning red lines based on UAV aerial photography, belonging to the field of construction engineering management. The method utilizes a UAV equipped with a high-resolution camera and an RTK system to plan a full-coverage flight path to collect aerial video, location information, and attitude parameters of the construction area, extracting keyframes and correlating the data. After sample construction and data augmentation, a semantic segmentation model is trained, and a binary mask of the construction area is generated by combining morphological operations. The coordinates of feature points on the planning red line CAD drawing are analyzed, and a red line mask is generated through a three-level transformation. The two masks are compared, and the boundary violation area is extracted. The proportion of the boundary violation area and the maximum boundary violation distance are calculated. Alarms are triggered in stages according to preset thresholds, and a monitoring report is output. This invention constructs an integrated automated monitoring system, solving the shortcomings of traditional manual inspections, such as low coverage, poor real-time performance, and high misjudgment rate, significantly improving the accuracy and efficiency of construction red line monitoring and providing strong support for compliance management.

The application discloses a kind of aerial video real-time labeling method and device.The application includes: after unmanned aerial vehicle takes off, the position of unmanned aerial vehicle is obtained to obtain the fence of unmanned aerial vehicle, and the fence of unmanned aerial vehicle is stored in fence library, wherein the range of the fence of unmanned aerial vehicle is circular area with the longitude and latitude point of unmanned aerial vehicle as center, and the radius of circular area is greater than 0 Real number;All point positions in the camera coordinate system of unmanned aerial vehicle are obtained, the point positions satisfying the equation of frustum are retained, and the point positions not satisfying the equation of frustum are removed;Point position is aggregated according to the height of unmanned aerial vehicle.Through the application, the problems of low efficiency, resource waste and inaccurate point scattering in related art of unmanned aerial vehicle real-time loading are solved.

The application discloses an aerial target detection method and device based on an attention mechanism, and a medium, relates to the technical field of image detection, and comprises the following steps: extracting a center coordinate sequence from a high-trust candidate frame, performing density aggregation to obtain an aggregation area frame, and performing boundary expansion, center padding and scale zooming to generate an aggregation cropped frame; constructing multi-scale features according to the aggregation cropped frame and a corresponding aerial video frame, and combining a candidate target pixel scale interval to perform scale prior-driven attention routing arrangement, to generate cross-flow gating weights and attention features; performing feature fusion on the aggregation area frame and the cross-flow gating weights through the attention features to obtain a real-time detection frame, and performing overlap conflict resolution and non-maximum suppression to generate an aerial target detection result set. The application realizes cross-flow gating feature fusion, is used for enhancing the image detection sensitivity of the aggregation area, reducing the missed detection rate and inhibiting the false merging of dense targets.

This invention discloses a method and system for color recognition and classification statistics of safety helmets used in water conservancy engineering construction based on unmanned aerial vehicles (UAVs). The method includes: acquiring aerial video data of a water conservancy engineering construction site taken by a UAV along a preset flight path; performing defogging and illumination compensation on the acquired video frames, and segmenting the original image into multiple overlapping sub-images; inputting the sub-images into an improved YOLOv8 model for safety helmet detection and color classification; tracking the detected targets using the ByteTrack algorithm, assigning a unique ID to each target, and introducing a global motion compensation module during the tracking process to offset the influence of the UAV's own motion; performing identity verification based on a temporal buffer and classification and counting based on spatial logic; mapping the safety helmet color to a preset job type, and outputting the number of workers on duty for each job type and / or early warning information. This invention enables efficient and refined management in complex environments during water conservancy engineering construction.

This invention relates to the field of cellular-connected drone video transmission technology, specifically disclosing a method for maximizing user experience quality in multi-drone aerial video transmission. This method is used in aerial video streaming systems that utilize multiple cellular-connected drones to capture video from different points of interest (PoI) regions and transmit the video to ground base stations (BS) and users, allowing ground users to share the drones' field of view. Based on this aerial video streaming system, this invention constructs an optimization problem. By jointly optimizing transmission scheduling, video playback rate, and drone trajectory design, it maximizes the minimum quality of experience (QoE) for all users, considering uplink interference and the trade-off between video quality and smoothness. Simulation results show that, compared to benchmark solutions, the proposed solution achieves a significant performance improvement and achieves a trade-off between video quality and smoothness.

The invention relates to the field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle video recognition method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: carrying out video frame sampling on a target aerial video to obtain a video frame set; obtaining spatial-temporal features, global scene features and local target features corresponding to the target aerial video according to the video frame set; fusing the global scene features and the local target features to obtain fused features; and obtaining a video recognition result according to the spatial-temporal features and the fusion features. According to the unmanned aerial vehicle video recognition method and device, the electronic equipment and the computer readable storage medium provided by the invention, the technical effect of improving the accuracy of an unmanned aerial vehicle video recognition result can be realized.

A measurement system for determining geo-reference data for a measurement area includes a first system receiving a video stream and selecting images therefrom to correlate with geographic x, y coordinates of the measurement area. The system includes a second system (i.e., aerial platform) to hover over the measurement area and a vertical camera scoring unit (VCSU). The aerial platform and VCSU further include an inertial measurement unit (IMU) to measure orientation and movement, a global navigation satellite system (GNSS) configured to determine geo-reference coordinates of a reference point, and a camera / mounting system configured to orient the camera towards the measurement area underneath the first system. The camera is oriented directly plumb downward to obtain plan view video across the measurement area and the second system computes a single absolute geodetic point position and communicates the video and data to the first system for point, distance, and velocity computation.

This invention relates to the field of video enhancement technology, specifically disclosing a method, system, storage medium, and electronic device for enhancing drone aerial video. The method includes: acquiring any video frame during a drone's aerial photography of a target area; extracting an image of the target area corresponding to the video frame from an engineering drawing of the target area; and fusing the target area image with the video frame to obtain an enhanced video frame corresponding to the video frame. This invention is unaffected by area boundaries and drone flight paths, features a simple and efficient calculation process, requires no complex modeling, and facilitates high-frequency, long-distance inspection work.

The invention relates to the technical field of video rendering, in particular to an AI aerial video intelligent rendering method based on a scenic area unmanned aerial vehicle, and the method specifically comprises the steps: obtaining aerial video stream data, holder stability augmentation log data and shooting scene metadata of the unmanned aerial vehicle; evaluating the disturbance degree of each frame of picture based on the video stream data and the stability augmentation log data; scene repair strength evaluation is carried out based on the shooting scene metadata; and combining and analyzing the picture disturbance degree evaluation result and the scene repair strength evaluation result to obtain a repair strategy of AI intelligent rendering of the current video segment. According to the method and the device, the problems of single evaluation dimension, scene perception deficiency and restoration strategy stiffness when coupling visual distortion caused by pan-tilt stability augmentation, natural medium motion and a complex depth-of-field structure is processed in the prior art are solved.

The present application relates to the technical field of unmanned aerial vehicle, especially to a kind of unmanned aerial vehicle aerial target detection repair method for optical distortion, comprising the following steps: obtaining unmanned aerial vehicle aerial video sequence, extract continuous frame image;Based on the statistical characteristics of thermal turbulence in a short time, construct space-time varying distortion model, distortion estimation is carried out to video sequence.The present application can effectively estimate and reverse optical distortion caused by thermal turbulence by constructing space-time varying distortion model and combining blind deconvolution technology, thereby significantly improving the definition and geometric consistency of unmanned aerial vehicle aerial image, this method makes full use of the time sequence information between continuous frames and turbulence statistical characteristics, realizes the accurate modeling of distortion field, and then restores the image with more detailed and clearer edges by inverse processing, provides high-quality input for subsequent target detection.