86 results about "Aerial video" patented technology

Processing circuitry may configure a system to implement a CAR-OnMap (“CAROM”) air framework for vehicle localization and traffic scene reconstruction using the aerial video of the traffic scene. Such a system may obtain aerial video of a traffic scene including vehicles that traverse the traffic scene and a satellite map image of the traffic scene as a distinct reference image. In such an example, processing circuitry may determine aerial image reference points within the aerial image which correspond to reference points in the satellite map image of the traffic scene. Processing circuitry may responsively generate calibrated images of the traffic scene from individual frames of the aerial video and determine unique keypoints on the vehicles in the traffic scene. In such an example, processing circuitry may track the vehicles across the individual frames of the aerial video utilizing the unique keypoints. Processing circuitry may output vehicle metrics for the vehicles.

The invention relates to the field of urban road modeling, in particular to an aerial photography data processing method and system for urban road modeling. The invention discloses an aerial photography data processing system for urban road modeling. The aerial photography data processing system comprises an aerial photography video acquisition module, a road modeling module, a photogrammetry module, a spatial registration module and a rendering synthesis module. According to the invention, on the basis of a spatial calibration mechanism of the original aerial image and the BIM model, redundant processes such as point cloud acquisition, data fusion and manual correction are eliminated, and linear processing from data acquisition to dynamic result output is realized; according to the architecture, the hardware resource dependency and the operation complexity are greatly reduced, and designers are endowed with the capability of completing large-scale scene visualization processing in a conventional engineering environment; according to the method, a transmission chain from original data to decision support is remarkably shortened, and important engineering scheme deduction and dynamic review have unprecedented timeliness.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a vehicle identification and traffic incident detection method and system based on the view angle of an unmanned aerial vehicle, and the method comprises the steps: obtaining the aerial traffic video data of the unmanned aerial vehicle and the flight control data of the unmanned aerial vehicle; performing target identification on the image in the video data by adopting a target detection algorithm to obtain vehicle information; tracking the vehicle in the video data by using a target tracking algorithm, and mapping the identified vehicle to a unified coordinate system in combination with the flight control data to generate vehicle trajectory data; constructing a comprehensive road description model based on the vehicle trajectory data and the vehicle information; and forming a traffic incident recognition module based on the comprehensive road description model, and recognizing the input aerial video according to a preset traffic incident recognition rule. According to the invention, by optimizing the vehicle identification algorithm, constructing the road description model and designing the efficient traffic event identification module, the application efficiency of the unmanned aerial vehicle aerial video in the traffic monitoring field is effectively improved.

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 discloses an unmanned aerial vehicle aerial video language positioning method in a disaster rescue scene, and the method comprises the steps: obtaining unmanned aerial vehicle aerial videos of a plurality of continuous frame images and a natural language description text of a to-be-positioned target, and jointly inputting the unmanned aerial vehicle aerial videos and the natural language description text into an unmanned aerial vehicle aerial video language positioning system, thereby obtaining a language positioning result; the unmanned aerial vehicle aerial video language positioning system comprises a video text feature extractor, a multi-modal multi-scale spatial-temporal feature coding module, a query modulation module and a multi-scale perception coordinate dynamic reasoning decoding module. And finally, outputting updated target bounding box coordinates through a multi-scale perception coordinate dynamic inference decoding module for representing the spatial position of the target referred by the natural language description text in the image. According to the method, deep semantic analysis and context understanding can be carried out on aerial video contents, and the practicability and efficiency of the unmanned aerial vehicle in security rescue tasks are remarkably improved.

The invention relates to a photovoltaic power station construction progress monitoring method based on unmanned aerial vehicle aerial video, and the monitoring method comprises the following steps: S1, building stage division and data set establishment; s2, improving a YOLOv8 target detection model; s3, model training and key component detection; s4, counting key components based on the video; s5, construction stage determination and progress calculation; the construction process of the photovoltaic power station is analyzed, the target detection algorithm is improved, the key components in the construction process of the photovoltaic power station are detected, the construction progress information of the photovoltaic power station of the target land parcel is obtained according to the type and the number of the detected key components, visualization, refinement, standardization and intelligentization of construction management of the photovoltaic power station are achieved, and the construction efficiency of the photovoltaic power station is improved. The construction quality and comprehensive benefits of a photovoltaic power station project are effectively improved, the problem that the construction progress statistical efficiency of an existing photovoltaic power station is low is solved, and the method has great significance in promoting digital and intelligent management of the photovoltaic power station project.

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.

The present application belongs to the field of drone inspection data processing technology, and in particular relates to a distribution construction project model association method based on drone inspection. It includes the following steps: obtaining aerial video images based on drone inspection; establishing a dynamic engineering model of the distribution construction project; visual processing of the dynamic engineering model of the distribution construction project; establishing an association model between the inspection camera position coordinates and the navigation perspective coordinates; establishing a camera position coordinate association model; establishing an inspection camera position coordinates and an optical perspective association model between the navigation perspective; The distribution construction project model association method based on drone inspection in the present application is mainly used to realize adaptive synchronous tracking of the dynamic engineering model with the inspection video during the planning and execution of the distribution construction project, and to realize synchronous acquisition of the model corresponding image data with the inspection video perspective, so as to facilitate the purpose of comparative analysis.

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.

The present invention provides a multimodal aerial video vehicle target tracking method that integrates scene prior knowledge, including the steps of associated region of interest extraction, local image edge detection, local image main color extraction, appearance weight vector calculation, scene prior information library construction, next frame target state variable prediction and target association result output. The method fully exploits the multimodal small target features, fully considers the temporal and spatial constraints between frames, and makes full use of scene prior information. The obtained target tracking result is more accurate, solves the difficulty of target tracking, namely the ID confusion problem, realizes stable and accurate aerial video vehicle target tracking, and is used to construct a real and accurate vehicle driving trajectory, extract traffic parameters from the trajectory, provide real and effective data support for intelligent vehicle research, lay the foundation for data-driven intelligent vehicle research methods, and support research in the fields of intelligent vehicle perception, decision planning, control, testing, etc.

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.

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 embodiment of the invention discloses a building planning model and aerial video overlapping display method and device and electronic equipment. A specific embodiment of the method comprises the following steps: acquiring shot project display aerial video information; obtaining to-be-planned building scene information; performing scene visualization processing on the to-be-planned building scene information to obtain three-dimensional scene information; performing key frame identification on the project display aerial video information to obtain a key frame parameter information set; performing flight path extraction on the key frame parameter information set to obtain flight path information; performing aerial view roaming on the three-dimensional scene information according to the flight path information to obtain a building scene roaming video; layering and overlapping the building scene roaming video and the project display aerial video information to obtain a project planning preview video; and transmitting the project planning preview video to a display terminal for display. According to the embodiment, the building model and the aerial video can be overlapped, and waste of computing resources is reduced.

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.

A display device is capable of displaying an aerial video image by utilizing retroreflection. The display device includes a light source configured to emit a video image from an emission surface; a first retroreflective member arranged on one end side of the light source to be inclined outward at a first inclination angle with respect to a normal to the emission surface; a second retroreflective member arranged on another end side of the light source to be inclined outward at a second inclination angle with respect to the normal to the emission surface; a first optical member configured to separate incident light into reflected light and transmitted light, which is arranged to face the first retroreflective member; and a second optical member configured to separate incident light into reflected light and transmitted light, which is arranged to face the second retroreflective member.

The present invention discloses a lane-changing trajectory classification method based on UAV aerial video, comprising the following steps: obtaining the rectangular frame of the position of the vehicle in a single-frame image of an autonomous vehicle and the coordinates of 4 BoundingBox points; obtaining the trajectory coordinates of each BoundingBox point and the historical lane-changing trajectory map of the vehicle; establishing a data set for each first sub-segment; dividing the historical lane-changing trajectory segment of the autonomous vehicle to obtain the first sub-segment; establishing a standard data set for each first sub-segment, dividing each standard data set according to the lane-changing stage to obtain a sub-standard data set; obtaining a standard two-dimensional image of vehicle lane-changing based on the sub-standard data set; obtaining the running data of the vehicle to be tested, obtaining a two-dimensional image of the lane-changing of the vehicle to be tested, and judging the lane-changing trajectory type of the vehicle to be tested; The present invention can effectively distinguish the lane-changing behaviors of autonomous and human-driven vehicles, provide accurate data support for lane-changing analysis under mixed traffic flow, and provide technical guarantee for subsequent traffic behavior research.

The present invention discloses a method for detecting and locating airport runway light anomalies based on aerial video analysis, characterized in that the method comprises the following steps: S1, inputting a runway light sample dataset into a YOLOV8 target detector for target detection training; S2, sequentially capturing video images via an unmanned aerial vehicle (UAV) and inputting them into a YOLOV8 target detector for target detection, and using the ByteTrack algorithm to associate target data and obtain a target tracking trajectory set; S3, extracting the coordinates of target trajectory points in the target tracking trajectory set, performing motion linear equation fitting, and obtaining the linear slope and Y-intercept; S4, performing unsupervised clustering of the target trajectory points according to motion characteristics using a Gaussian mixture model to obtain a left sideline light target trajectory set, a middle line light target trajectory set, and a right sideline light target trajectory set; and performing runway light anomaly determination and outputting target and location prompts. The present invention can rapidly detect lighting anomalies of runway centerline lights and sideline lights, providing technical support for airport security.

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 present invention relates to the field of UAV communication technology, and specifically discloses a method and system for minimizing the energy consumption of multi-antenna UAV communications. First, an aerial video surveillance system based on a multi-antenna rotorcraft UAV is constructed, allowing a multi-antenna UAV (UAV) to provide services to multiple GUs (ground users) at the same time. Then, by jointly optimizing the UAV's flight trajectory, flight time, and transmit beamforming, an optimization problem is constructed with minimizing the UAV's total energy consumption as the optimization goal, while meeting the user's QoS requirements. The optimization problem is further solved. In order to solve the optimization problem, a path discretization method is first used in combination with a golden section search method to determine the UAV's flight time and flight trajectory, thereby minimizing the UAV's propulsion energy consumption, and then minimizing the UAV's communication energy consumption. Simulation results show that the method and system are significantly superior to existing benchmark solutions in terms of energy consumption, demonstrating high efficiency and practicality.