156 results about "Aerial video" patented technology

In some implementations, a camera may be disposed on an autonomous aerial platform. A user may operate a smart wearable device adapted to configured, and / or operate video data acquisition by the camera. The camera may be configured to produce a time stamp, and / or a video snippet based on receipt of an indication of interest from the user. The aerial platform may comprise a controller configured to navigate a target trajectory space. In some implementation, a data acquisition system may enable the user to obtain video footage of the user performing an action from the platform circling around the user.

The invention discloses a real-time panoramic image stitching method of aerial videos shot by an unmanned plane. The method comprises the steps of: utilizing a video acquisition card to acquire images which are transmitted to a base station in real time by an unmanned plane through microwave channels, carrying out key frame selection on an image sequence, and carrying out image enhancement on key frames; in the image splicing process, firstly carrying out characteristic detection and interframe matching on image frames by adopting an SURF (speeded up robust features) detection method with good robustness; then reducing the series-multiplication accumulative errors of images in a frame-to-mosaic image transformation mode, determining images which are not adjacent in time sequence but adjacent in space on a flight path according to the GPS (global positioning system) position information of the unmanned plane, optimizing the frame-to-mosaic transformation relation, determining image overlapping areas, thereby realizing image fusion and the panoramic image construction and realizing real-time effect of carrying out flying and stitching simultaneously; and in image transformation, based on adjacent frame information in a vision field and adjacent frame information in airspace, optimizing image transformation to obtain the accurate panoramic images. The stitching method has good real-time performance, is fast and accurate and meets the requirements of application occasions in multiple fields.

A method and system are disclosed for creating a real-time, high accuracy mosaic from an aerial video image stream by applying orthorectification of each original video image frame using known ground control points, utilizing a photogrammetric model resolving the object image into pixilation, applying shading to the pixellation, and mosaicking the shaded pixilation of several orthorectified images into a mosaicked image where the mosaicked image is then scaled to the known original image dimensions.

The invention discloses a detection and tracking method of a moving small target in an aerial shot video. The method includes the steps of 1, collecting images, 2, extracting SURF feature points, 3, carrying out grouped matching on the images, 4, obtaining an affine matrix, 5, obtaining a difference image, 6, carrying out opening operation, 7, extracting a target area, 8, determining a target template, 9, determining a target detection area, 10, extracting and matching the feature points, 11, determining a registering center position of a target, 12, determining a target central position, and 13, determining the length and the width of the target. The method has good real-time performance and robustness in the target tracking process, and can obtain smooth target moving tracks in the target tracking process.

The invention provides an automatic lane line identification method based on low-altitude aerial images. The automatic lane line identification method based on the low-altitude aerial images is applied to the field of intelligent transportation. The automatic lane line identification method based on the low-altitude aerial images comprises the steps that (1) an original image of a road is collected through low-altitude aircrafts, and the situation that the shooting angle of the aerial photography road is in the horizontal direction, and the area of the road is arranged in the middle of the image is ensured; (2) the collected image is converted into a grayscale image, the contrast ratio is improved, the grayscale image is copied, and an edge detection image and a binarization image are obtained according to the grayscale image; (3) connected areas are detected in the edge detection image and the binarization image, and characteristics of the connected areas are recorded; (4) the connected areas are deleted according to the number of pixels of the connected areas, the number of connected boundaries, the size of a bounding rectangle and variance values to obtain a central road line; (5) search is carried out on the two sides of the central road line to find roadside lane lines. The automatic lane line identification method based on the low-altitude aerial images is easy and convenient to calculate, and high in arithmetic speed and reliability, the road portion in an aerial photography video can be effectively extracted, straight roads and curve roads in the aerial images can be detected, and the automatic lane line identification method based on the low-altitude aerial images cannot be disturbed by background changes, and is high in accuracy.

In some implementations, a camera may be disposed on an autonomous aerial platform. A user may operate a smart wearable device adapted to configured, and / or operate video data acquisition by the camera. The camera may be configured to produce a time stamp, and / or a video snippet based on receipt of an indication of interest from the user. The aerial platform may comprise a controller configured to navigate a target trajectory space. In some implementation, a data acquisition system may enable the user to obtain video footage of the user performing an action from the platform circling around the user.

The invention discloses an aerial photographic video tracking method based on scale self-adaptive and shielding handling. The method comprises the steps that as for each frame of input image of an aerial photographic video, the peak value of a KCF tracker response function under the current scale is calculated, whether a target is shielded is judged, updating of the tracker parameters is stopped if the judgment result is yes, or scale change detection is continued; and a large scale or a small scale is calculated through a threshold limiting method every time, the optical scale for describing the current target state is detected and the tracker parameters of the current scale are updated. The method has great robustness and real-time performance in the process of tracking the target in the aerial photographic video so that accurate and continuous tracking of the aerial photographic target can be realized.

A system and method for aerial video traffic analysis are disclosed. A particular embodiment is configured to: receive a captured video image sequence from an unmanned aerial vehicle (UAV); clip the video image sequence by removing unnecessary images; stabilize the video image sequence by choosing a reference image and adjusting other images to the reference image; extract a background image of the video image sequence for vehicle segmentation; perform vehicle segmentation to identify vehicles in the video image sequence on a pixel by pixel basis; determine a centroid, heading, and rectangular shape of each identified vehicle; perform vehicle tracking to detect a same identified vehicle in multiple image frames of the video image sequence; and produce output and visualization of the video image sequence including a combination of the background image and the images of each identified vehicle.

Disclosed herein is a mechanized lift platform for extending a camera and sensor, individually or collectively, out of a flying craft and retracting the camera and sensor back into the flying craft upon completion of use. The camera and sensor in the fully extended position provides a full field of view. The camera and sensor is extended and retracted through concealment doors. The concealment doors are closed when the camera and sensor are in the fully retracted position such that the lift platform and camera and sensor are not subject to any outside environment and the flying craft is able to retain its original flying characteristics. The camera and sensor mechanized lift platform has particular application for aerial photography, video, sound collection and multimedia. A method of capturing images, sounds and data comprising the steps of: Providing a device for extending a camera and sensor outside a flying craft whereby the camera and sensor can be completely retracted into the aircraft, having a platform structure to mount a camera and sensor, a power source, mechanized liner motion structure installed in flying craft to stabilize and guide the camera and sensor mounting platform during extension and retraction, concealment doors to open and close, relay switches to the flying craft cockpit and / or cabin to operate the lift platform and concealment doors, viewing and recording equipment connected to camera and sensor; and opening the concealment doors and extending the camera and sensor during flight to collect images, sounds and data or any combination thereof and display said collected images in a live graphical display in aircraft or to a remote site and upon completion of camera and sensor activities, retracting the lift platform inside the aircraft and closing the concealment doors. In a preferred embodiment of the invention the camera and sensor mechanized lift platform is extended through opened concealment doors during flight to collect images, sounds and data or any combination thereof and upon completion of camera and sensor activities, the lift platform is retracted inside the aircraft and the concealment doors are closed such that the original flying craft speed, maneuverability and aerodynamic characteristics are not altered.

The invention brings forward a rapid and automatic mosaic technology of aerial video in a search and tracking system. The technology provided by the invention comprises the following steps: two continuous frames of color images in an aerial video sequence are converted to grayscale images; three-valued processing is carried out on the grayscale images by an image three-valued method, edge feature information images are extracted by an edge detection sobel operator and feature points are extracted by an Harris algorithm; feature point matching is completed by an optical flow pyramid algorithm; optimal homography matrix of the color images is calculated by combining a DLT algorithm and a RANSAC algorithm; panoramic image mosaics is completed after image correction of the color images by correction geometric transition matrix; and dynamic regulation of image three-valued upper and lower thresholds and a threshold of the edge detection sobel operator is carried out during the process of panoramic image mosaics. According to the invention, image mosaic accuracy and robustness are raised.

The invention discloses an unmanned aerial vehicle sharing method, server, client and system, and belongs to the technical field of an unmanned aerial vehicle. The method comprises following steps of receiving a sharing request sent by the client; sending at least one piece of aerial photographing region introduction information to the client so that a user selects an aerial photographing region; receiving the aerial photographing region selection information which is input by the user and is sent by the client; determining the aerial photographing region selected by the user according to the aerial photographing region selection information; building communication connection between the control device of at least one in all sharable unmanned aerial vehicles corresponding to the aerial photographing region selected by the user and the client; sending an obtained control instruction input by the user to the control device by the client so as to control the unmanned aerial vehicle to fly or aerially photograph; receiving an aerially photographed video which is photographed by the unmanned aerial vehicle and is sent by the control device, wherein the control device is used for controlling the unmanned aerial vehicle to fly or aerially photograph according to the user control instruction sent by the client and sending the aerially photographed video which is photographed by the unmanned aerial vehicle to the client. According to the method, the server, the client and the system provided by the invention, the user can select own favorite aerial photographing region and then control the unmanned aerial vehicle corresponding to the selected aerial photographing region; and the user experience is improved.

Multi-source multi-modal activity recognition for conducting aerial video surveillance comprising detecting and tracking multiple dynamic targets from a moving platform, representing FMV target tracks and chat-messages as graphs of attributes, associating FMV tracks and chat-messages using a probabilistic graph based mapping approach; and detecting spatial-temporal activity boundaries.

The invention relates to an aerial photographed video target tracking method based on related filtering and significance testing. Through the tracking method based on related filtering and significance testing, a direction gradient histogram characteristic and a gray-scale characteristic are utilized for realizing high robustness of a tracking result to factors such as illumination change, noise and shield. Particularly when a target is severely shielded and tracking failure occurs, the aerial photographed video target tracking method realizes target detection through a redetection mechanism, so that long-time continuous tracking of the target can be realized. Furthermore, a significance detecting method based on a residual spectrum theory realizes simple and quick calculation. Redetection to the target can be realized through a relatively low calculation cost. A requirement for real-time performance of an algorithm in aerial photographing video tracking can be effectively satisfied.

The invention discloses an unmanned aerial vehicle aerial video moving small target real-time detection and tracking method. The method comprises the steps of obtaining a current background model through modeling of a single Gaussian background model, distinguishing whether pixel points are foreground or background after obtaining the single Gaussian background model so as to obtain a foreground image, performing sparse optical flow analysis on the obtained foreground image, and obtaining a tracking point set; Hierarchical clustering is carried out on the tracking points; obtaining an outer frame of the tracking target, deducting a tracking target in an outer frame of the tracking target obtained by detecting each frame of foreground image; a follow-up to-be-tracked list is formed, a feature vector is extracted from each tracked target through a deep neural network, prediction is conducted on each tracked target through a Kalman filtering algorithm, matching is conducted through a Hungarian algorithm, the tracked list is updated, and the updated tracked targets are obtained. According to the invention, a single Gaussian model is adopted to carry out background modeling, the detection time consumption is reduced, and the overall efficiency is improved.

The invention provides a virtual three-dimensional map establishing method and belongs to the field of electronic maps. The method comprises the steps that 1, an aerial video of a target building in atarget region is acquired, wherein the aerial video contains coordinate information, synchronously recorded, of a camera under a world coordinate system; 2, the aerial video is divided into video frames according to a time order, and two-dimensional coordinates of building image matching points in two frame images under the camera coordinate system are acquired; 3, the two-dimensional coordinatesof the building image matching points in the two selected frame images are converted according to internal parameters and external parameters of the camera in combination with the coordinate information of the camera under the world coordinate system, three-dimensional coordinates of the building image matching points under the world coordinate system are obtained, then three-dimensional reconstruction is performed on the building images, and building three-dimensional images are obtained; and 4, the building three-dimensional images are transplanted to corresponding positions in a satellitemap or a two-dimensional map or an existing three-dimensional map, and the virtual three-dimensional map containing the target building is obtained.

The invention discloses an automatic target tracking method for aerially photographed videos and aims to solve the technical problem that an existing aerially photographed video target tracking method based on stable image detection and data association is high in tracking error rate. According to the technical scheme, firstly, a pyramid optical flow method is used for extracting robust optical flow track features rather than simple motion foreground segmentation; secondly, motion constraint, position constraint and existence duration constraint are utilized for effective management of an optical flow track; lastly, clustering in time dimension is conducted according to a spatial clustering result of a plurality of continuous frames to effectively solve the problem of spatial clustering errors under the conditions of target intersection and a close range between targets. According to the automatic target tracking method for the aerially photographed videos, a target tracking result is obtained on the basis of spatial clustering, and therefore the tracking error rate is lowered. Tests prove that the tracking error rate is lowered by 10% from 18% in the background art to 8%.

The present invention discloses a self-encoding neural network-based wind turbine visual detection system. According to the system, a part of frames of wind turbine images in an unmanned aerial vehicle aerial video are pre-processed, and the pre-processed images constitute a training sample set and a test sample set for wind turbine visual detection; a self-encoding neural network is built; positive and negative samples in the training sample set are arranged in a self-encoder so as to be pre-trained; an activation quantity which is obtained through calculation is adopted to replace original input, so that a new sample feature set is formed; new feature vectors are inputted to an established BP neural network, so that the two-pass classifier of a wind turbine can be obtained; the sample images in the test set are arranged in the classifier so as to be tested; areas containing the wind turbine are searched through using a random sliding window method; and screening is performed throughnon-maximum suppression, so that a final wind turbine detection area can be obtained. The system of the invention can be widely applied to fields such as pattern recognition and intelligent inspectionof wind power generation.

The invention relates to an expressway vehicle detection method based on improved visual background extraction, which comprises the steps of 1, initializing a read-in image, wherein an input color image is converted into a grayscale image; 2, carrying out background molding on the image, carrying out background initialization if the image is a first frame image, and otherwise, carrying out background updating so as segment out the background; 3, segmenting out a road surface from the foreground; 4, carrying out eight-neighborhood filling on the image; 5, removing a lane line by using a characteristic that the lane line is narrow; 6, filling the internal part of a vehicle by using a longitudinal filing method; and 7, extracting a vehicle area and drawing an external rectangular frame of the vehicle area. According to the invention, vehicles on the expressway in aerial video of an unmanned aerial vehicle are detected by adopting a mode of image processing, the cost is low, the detection precision is high, the real-time performance is good, and the application range is wide.

The invention provides a small target detection method based on an unmanned aerial vehicle, and the method comprises the steps: enabling the unmanned aerial vehicle to fly autonomously to shoot a video; Sequentially reading two frames in the video sequence, calculating a Hessian matrix, and extracting key points in the image; Calculating the Euclidean distance between the key points, and matchingthe key points in the two images according to the Euclidean distance and the Hessian matrix; removing noise points in the key points, and calculating and calibrating a perspective transformation matrix between every two frames of images; And carrying out subtraction on the calibrated current frame and the reference frame to obtain a difference image, carrying out binaryzation, morphological operation, reconstruction and the like on the difference image in sequence, and extracting and marking a foreground target in the image. According to the method, small targets in the aerial video can be effectively extracted and detected through similarity of backgrounds, meanwhile, computing resources of the unmanned aerial vehicle are fully used, and the online target detection capability of the unmanned aerial vehicle is improved.

The invention discloses an aerial video highway lane line detection method based on line spacing characteristic point clustering, which comprises the following steps: reading video frame; Image segmentation; equilibrium from a large number of data points, fully extract the characteristic points of each lane line; the similarity matrix is constructed based on the similarity measure method of line spacing, and the characteristic points of different lane lines are clustered. The cubic B-spline model of lane is established and the parameters of the model are estimated by the improved RANSAC algorithm; correction and prediction of lane parameters. The invention detects and pretreats the edge of the expressway in the video of the aerial shooting of the expressway, Reducing the processing time ofthe next frame, effectively removing the interference pixels outside the freeway, has a better clustering effect on lane feature points, can get more accurate and more stable lane fitting effect, andcan achieve real-time processing effect.

The invention relates to a drone control signaling and a data transmitting method based on a mobile network. The drone comprises: a broadband data transmitting system applicable to high-definition video transmission of the drone, a control signaling transmitting system for receiving drone state data and sending drone control commands, a drone cloud service system for forwarding data, and a groundstation system for receiving video and remotely controlling the drone. According to the drone control signaling and data transmitting method based on a mobile network, a 4G-LTE network card is mountedon an airborne computer of the drone end, the telemetry data and the aerial video data in the flight process of the drone are compressed and encoded by the airborne computer, and transmitted via the4G mobile network. The activity range of the drone is no longer limited by the data link transmission distance by connecting the drone into the mobile internet, and the application field of the dronecan be greatly expanded.

The invention discloses an intersection vehicle queuing length detection method based on an aerial video. The intersection vehicle queuing length detection method comprises the following steps: step 1, registering the aerial video; step 2, setting a vehicle queuing length detection region at an intersection entrance path; step 3, detecting a red light state; step 4, when a lane is at the red light state, detecting moving vehicles in the pre-set vehicle queuing length detection region by using a three-frame difference method; step 5, determining a vehicle queuing region; step 6, detecting the quantity of static vehicles of the vehicle queuing region and calculating an actual vehicle queuing length by using an actual distance Scale represented by a calibrated image unit pixel; and step 7, repeating the steps to realize continuous vehicle queuing length detection. According to the intersection vehicle queuing length detection method, the vehicle queuing length detection is carried out by the aerial video and mutual shielding and size changes of the vehicles in the monitored video are overcome, so that an intersection vehicle queuing condition is directly represented and the detection precision is extremely improved.

Provided are an aerial stereoscopic video streetscape system based on a navigation airship and an implement method. The system acquires aerial video streetscape by a viewing mode combining automatic flying and manual flying of a navigation airship, and solves a difficulty in streetscape data acquisition and update at high efficiency and low cost. A rotor wing airship captures aerial panoramic videos, and divides layers according to a flying height and by using latitude and longitude as coordinates, thereby forming the aerial video streetscape of a city. The system has wider view and higher height, displays the stereoscopic space of the city in an omnibearing way, effectively prevents a case that current ground streetscape is restricted by lack of roads, obstruction of buildings, ground fluctuation, and blind angles, may cover any area of the city in the air, replaces static streetscape with dynamic video streetscape, and may display the real space really and dynamically in a large data bulk.

The invention discloses a shared control method and device for an unmanned aerial vehicle. The method comprises the following steps of receiving a control request message sent by a server and / or a client; controlling the unmanned aerial vehicle to fly to a shared initial position in a preset flight area from a parking base; judging whether the unmanned aerial vehicle reaches the shared initial position, if yes, sending an in-place prompting message to the client; sending an aerial video shot by the unmanned aerial vehicle to the client; receiving an user control instruction sent by the client; controlling the unmanned aerial vehicle to fly or carry out aerial photography in the flight area according to the user control instruction; and after receiving a shared end instruction sent by the server and / or the client or obtaining low battery information of the unmanned aerial vehicle, controlling the unmanned aerial vehicle to return to the parking base or the other parking base when the unmanned aerial vehicle takes off. According to the control method or system for the unmanned aerial vehicle, which is provided by the invention, the automation control for network sharing of the unmanned aerial vehicle is realized.

The invention relates to a moving target detection technology, in particular to a deep inverse reinforcement learning-based target detection method in an unmanned aerial vehicle aerial video, which ischaracterized by at least comprising the following steps: 1, establishing a deep inverse reinforcement learning model; 2, performing model strategy iteration and algorithm implementation; 3, selecting and optimizing key parameters of the model; and 4, outputting a moving small target detection result. According to the target tracking method in the unmanned aerial vehicle aerial video, complex tasks can be solved, and award return delay can be avoided.

The invention discloses an unmanned aerial vehicle real-time video sharing method and system. The sharing method comprises the following steps: (1) starting up aerial photographing equipment or image photographing equipment; (2) photographing by taking a media stream timestamp manner and / or a text timestamp manner and encoding photographed data to generate a media stream; (3) transmitting the media stream to a cloud server through a communication network for carrying out real-time sharing of data. With the adoption of the unmanned aerial vehicle real-time video sharing method and system, real-time sharing and processing of unmanned aerial vehicle images / videos is realized, the speed is rapid and the efficiency is high; meanwhile, all-directional and high-precision sharing of a plurality of types of information is also realized.

The invention discloses a driver lane-changing behavior analysis method based on an aerial photography video vehicle trajectory. Based on the image tracking technique, the movement video data of a vehicle, collected by the aerial photography of an unmanned aerial vehicle, are extracted and processed. After that, through the data fitting, the mapping and the conversion of coordinate systems, a corresponding movement track graph, a displacement space-time diagram in the direction of lanes and a lane deviation fluctuation diagram perpendicular to the direction of lanes are obtained. According tothe invention, a vehicle trajectory processing and conversion method is provided by the invention. By the method, vehicle trajectories in a video can be efficiently, conveniently and accurately extracted and processed. The method has important significance on the research and the development of trajectory analysis-based relevant traffic phenomena and driver characteristics.