182 results about "Image tracking" patented technology

The invention relates to the technical field of ecological monitoring, in particular to an agricultural ecological environment monitoring method and system based on digital twinning, and the method comprises the following steps: obtaining monitoring data in a farmland, constructing a mapping relation, carrying out time sequence arrangement, merging multi-source data, calling a virtual grid to update a three-dimensional model, generating a mapping result, and analyzing a crop growth image. And tracking environmental changes, comparing ecological stability intervals, extracting risk trends, calling the three-dimensional model, and generating a risk early warning interface layer. According to the method, the continuous data of the agricultural ecological environment is acquired, and the mapping relation is constructed to perform time sequence arrangement on the information, so that the response speed and the processing precision of the change of the agricultural ecological environment are improved, the monitoring and management of the crop growth environment are optimized, and the environment change can be visually displayed through the expression of the real-time updated three-dimensional environment model; by comparing humidity and temperature changes and screening space segments, careful monitoring and accurate early warning of environmental changes are enhanced.

An unmanned aerial vehicle (UAV) is disclosed that includes a retractable payload delivery system. The payload delivery system can lower a payload to the ground using a delivery device that secures the payload during descent and releases the payload upon reaching the ground. The location of the delivery device can be determined as it is lowered to the ground using image tracking. The UAV can include an imaging system that captures image data of the suspended delivery device and identifies image coordinates of the delivery device, and the image coordinates can then be mapped to a location. The UAV may also be configured to account for any deviations from a planned path of descent in real time to effect accurate delivery locations of released payloads.

Disclosed are approaches that may provide image-guidance to interventionalists by providing true 3D visualization and quantitative feedback in real-time. A guidance system may allow a physician to manipulate a medical device and see a 3D rendering with quantitative feedback floating in mixed reality, next to standard monitors. Image tracking may detect and co-register the medical device's 3D position using, for example, bi-plane C-arm X-ray fluoroscopy and provide a 3D trajectory as quantitative feedback. Patterns in a fluoroscopic image may be used to accurately determine an object's z-position from a single angle projection.

According to the microscopic cell image tracking method and device based on the graph neural network, the electronic equipment and the storage medium provided by the invention, a training set cell image is acquired and a training label is marked, a cell segmentation model is trained according to the training set cell image, and other data sets are predicted according to the cell segmentation model; according to the tracking method provided by the invention, the dependence on manual design can be reduced, the feature expression ability can be improved, the relationship between the cells can be modeled through the graph structure, the tracking precision under the condition of dense and shielded cells can be improved, and the tracking accuracy can be improved. Meanwhile, in combination with time sequence data, the time continuity of cell movement is captured, and the tracking stability is improved.

The invention relates to the technical field of pan-tilt image tracking control. The method comprises the following steps: carrying out real-time coding processing on a target tracking image frame to obtain a video data stream, obtaining wireless communication state information according to the video data stream, obtaining an optimized video data stream based on the wireless communication state information, and utilizing a high-speed wireless communication channel to carry out real-time coding processing on the target tracking image frame to obtain the target tracking image frame. Transmitting the optimized video data stream to the mobile terminal from the handheld holder in real time to obtain an image frame for display, performing display processing on the image frame for display in the mobile terminal, presenting a tracking picture frame sequence synchronous with the video data stream, obtaining touch gesture input information of a user on the mobile terminal, and displaying the touch gesture input information on the mobile terminal. And according to the touch gesture input information, generating a corresponding tracking control instruction, and adjusting a tracking control item of the handheld holder by using the tracking control instruction. The cloud platform tracking control linkage method has the effect of cloud platform tracking control linkage based on the user touch operation.

The invention discloses a multi-mode fusion mixed reality multi-person space alignment method and system. The method comprises the following steps: step 1, system initialization; step 2, space alignment of the VPS mode; step 3, carrying out space alignment of the image tracking mode; step 4, carrying out space alignment of the object tracking mode; step 5, space alignment of the center cooperation mode; step 6, carrying out space alignment in a double-anchor mode; 7, real-time updating and dynamic adaptation are carried out; and step 8, multi-person synchronization. By combining various space alignment schemes, the precision and adaptability of space alignment can be effectively improved, and the method is suitable for various environments and use scenes. The system provided by the invention has high flexibility, can perform adaptive adjustment according to real-time environment change and user requirements, and ensures that the virtual object keeps a consistent alignment effect in a plurality of user viewing angles.

The invention relates to the technical field of unmanned aerial vehicle testing, in particular to an array type high-energy laser countering unmanned aerial vehicle testing method and device, and the method comprises the steps: preparing a testing device and an unmanned aerial vehicle; performing a laser countering unmanned aerial vehicle test; the ablation process is observed, the ablated morphology of the unmanned aerial vehicle is analyzed, and the ablation area radius and the ablation depth of the tested unmanned aerial vehicle are obtained; finite element simulation is carried out on the test process, and the simulated unmanned aerial vehicle ablation area radius and ablation depth are obtained; and comparing the radius and the ablation depth of the ablation area of the unmanned aerial vehicle obtained by simulation and test, adjusting finite element simulation parameters to enable a simulation result to be matched with a test result, and exporting an ablation depth-time change curve of the key point of the simulated ablation area. According to the invention, a scheme capable of making up for insufficient definition of remote image tracking equipment is provided, the number of high-power lasers is increased, and the unmanned aerial vehicle is hit in a larger range; multi-point high-temperature ablation of the target unmanned aerial vehicle is realized, and the probability of striking vulnerable parts of the unmanned aerial vehicle is increased.

Systems and methods for tracking a location of an object of interest through a sequence of medical images are provided. First and second input medical images of a patient are received. The first input medical image comprises an annotation of a location of an object of interest. Features are extracted from the first and the second input medical images. A location of the object of interest in the second input medical image is determined using a machine learning based location predictor network based on the annotation of the location of the object of interest in the first input medical image and the extracted features from the first and the second input medical images. The location of the object of interest in the second input medical image is output. The machine learning based location predictor network is trained based on a comparison between 1) locations of a particular object in a sequence of training images determined during a forward tracking of the particular object through the sequence of training images and 2) locations of the particular object determined during a backward tracking of the particular object through the sequence of training images.

The present invention provides a method for quantifying moving heterologous nanoparticles in a suspension by imaging. The quantifying method comprises acquiring at least one z-stack of images within the suspension; tracking the nanoparticles in the images to identify unique nanoparticles; and enumerating the unique nanoparticles. Also provided is a method for characterizing size distribution of moving heterologous nanoparticles in a suspension by imaging. The characterization method comprises acquiring time lapse images; tracking the nanoparticles in the images to identify unique nanoparticles; determining the locations of each of the unique particles; determining the size of each of the unique nanoparticles; and aggregating the sizes of the unique nanoparticles.

The invention discloses a non-motor vehicle passenger-vehicle identification method and system in a crowded scene, and belongs to the technical field of intelligent traffic management. In order to solve the problem of accurate recognition of non-motor vehicles, persons and vehicles in a crowded scene, image data of a traffic intersection is collected through a traffic camera, target detection processing is performed on the image data by using a target detection model, and a person target and a non-motor vehicle target are detected to obtain a person target frame and a non-motor vehicle target frame; judging whether the scene category to which the image data of the traffic intersection belongs is a crowded scene or not to obtain crowded scene image data; personnel key points and non-motor vehicle key points in the crowded scene image data are extracted; associating the personnel key points with the non-motor vehicle key points to determine personnel key points associated with the non-motor vehicle key points to obtain a personnel-vehicle association target; and identifying whether the person-vehicle associated target has traffic violation behaviors or not through an image tracking technology, and if so, identifying the identity information of the person target for traffic violation early warning and processing.

The invention relates to the technical field of image tracking, in particular to an abnormal person tracking method and system based on a face image, and the method comprises the steps: carrying out the static identity feature extraction and dynamic behavior feature extraction of a stable face feature basic image, and obtaining a static identity feature set and a dynamic behavior feature set; performing space-time alignment processing on the static identity feature set and the dynamic behavior feature set to obtain a space-time aligned feature set; performing feature decoupling processing based on the space-time alignment feature set to obtain independent identity features and independent behavior features; and carrying out abnormal mode identification on the independent behavior characteristics to obtain an abnormal behavior type classification result. According to the invention, cross-scene and cross-time-frame personnel tracking can be realized by combining the static identity features and the abnormal behavior classification result; an abnormal person moving track is drawn according to the timestamp and the space coordinate of the abnormal behavior, track coherence is improved based on continuity scores, and tracking interruption caused by view angle switching or time intervals is avoided.

The invention relates to the field of medical instruments, in particular to a detection system for assisting accurate positioning of chest lesions, which comprises a data acquisition module for acquiring preoperative CT image data; the three-dimensional reconstruction module is used for converting into a three-dimensional model; the coordinate construction module is used for establishing a unified virtual coordinate system and determining key positioning points; the virtual cutting module is used for simulating different paths and angles to perform pathological section on the three-dimensional model; the image tracking module is used for capturing image data in the body of the patient in real time during an operation and fusing the image data with the three-dimensional model based on the key positioning points; the image processing module is used for carrying out image acquisition and processing on the pathological section; and the space verification module is used for carrying out space position matching on the image data and the three-dimensional model and calculating the similarity between the image and the corresponding region and the overlap ratio index of the lesion position. The objective of the invention is to realize accurate spatial position correspondence between the lesion area in the preoperative CT image and the actual resection pathological section in the operation.

The invention discloses a lightweight multi-scale biomedical image segmentation method, and relates to the technical field of medical image processing, and the method comprises the steps: collecting and obtaining a reference position medical image for a target operation task; performing feature importance and feature dominance analysis in combination with key texture feature information of a target operation, and calibrating a datum positioning reference point; real-time image tracking segmentation is carried out, rapid positioning alignment and multi-scale feature extraction are carried out on continuous medical images, and a multi-scale feature map is generated; introducing a feature fusion mechanism, and performing layer-by-layer fusion on the multi-scale feature map to obtain a fused feature map; and obtaining an image segmentation result. The technical problems that existing biomedical image segmentation is difficult to adapt to complex and diversified image segmentation tasks, so that the image segmentation precision is low, and the reliability and safety of surgical navigation are reduced are solved, high-precision image segmentation is realized, and the technical effect of improving the reliability and safety of surgery is achieved.

The invention provides a device and a method for automatically measuring compaction settlement by adopting binocular vision, and relates to the technical field of computer vision, the compaction settlement is calculated by calling and analyzing interactive data of an equipment scheduling execution module, a visual system construction module, a rammer identification and positioning module, an image tracking and collecting module and a coordinate calculation execution module; and a compaction settlement change curve is obtained. The technical problems that in the prior art, due to the fact that compaction settlement monitoring depends on manual measurement or uses a contact type sensor to conduct compaction settlement monitoring, timeliness of compaction settlement measurement is insufficient, the measurement result is prone to being influenced by manual work, and construction risks in the foundation compaction process are increased are solved. Reliable technical support is provided for real-time monitoring of the foundation compaction effect in the rammer construction process, the automation degree and precision of compaction settlement measurement are achieved, and the technical effects of providing reliable guarantee for the foundation compaction quality in the construction process are achieved.

The invention discloses a tracking frame maintaining method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target vehicle with a missing tracking frame according to video data, and determining the motion state of the target vehicle; when the motion state is static, acquiring a first motion track of the target vehicle in the video data and a second motion track of surrounding vehicles corresponding to the target vehicle; determining a target front vehicle in the surrounding vehicles according to a distance comparison result of the first movement track and the second movement track; and determining that the motion state of the target front vehicle is static, determining that the target vehicle is in a queuing state, and taking a historical tracking frame of the target vehicle in the video data as a missing tracking frame of the target vehicle. According to the embodiment of the invention, the data of the lost tracking frame in the image tracking process of the motor vehicle can be repaired, the loss probability of the tracking target can be reduced, the accuracy of subsequent target detection can be improved, and the application scene of target tracking can be expanded.

The invention provides an acupoint identification marking method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a dynamic video of which a shot object is a human body, and identifying all acupoint names contained in the dynamic video; and selecting a target acupoint from all the contained acupoint names, and tracking and marking the position of the target acupoint in the dynamic video based on an image tracking algorithm and inertial navigation attitude data. The embodiment of the invention provides an acupoint identification marking method and system, electronic equipment and a storage medium, which better utilize the time sequence continuity of video frames to carry out acupoint identification, and solve the problem of tracking failure or tracking precision reduction when approaching a target acupoint weak texture area by fusing video attitude and inertial navigation attitude data, thereby improving the accuracy of the acupoint identification marking. Meanwhile, after acupuncture point marking and tracking, follow-up interactive guidance on actual acupuncture point operation can be facilitated, and the practicability after acupuncture point recognition is greatly improved.

The invention provides a train carriage bulk material leveling method and system based on a material leveling machine and a medium, and relates to the technical field of bulk material loading, and the method comprises the following steps: S1, carrying out real-time synchronous scanning on a train carriage through a plurality of laser radar scanners, and obtaining real-time point cloud data of the carriage; s2, extracting key feature point cloud data through a 3D sparse convolutional neural network based on the real-time point cloud data of the carriage; and S3, processing the key feature point cloud data through point cloud voxelization, and projecting on a plane to generate a two-dimensional distribution diagram. According to the method, multi-laser radar real-time synchronous scanning is combined with the 3D sparse convolutional neural network, two-dimensional mapping is performed through voxelization processing, and the moving position required by the leveling machine in the next step is tracked and predicted according to the two-dimensional image, so that the point cloud data processing efficiency is remarkably improved; the problem of insufficient real-time performance caused by large data volume in a traditional method is effectively solved, and meanwhile the requirements for high real-time performance and high precision of material leveling operation are met.

The invention discloses a relay tracking application method and system for large and medium-sized ships in open seas, and the method comprises the following steps: S1, obtaining the current position information of a target ship, and delimiting a circular region of interest based on the current position information; s2, performing overhead satellite calculation within half an hour based on the region of interest; the current position of the target ship is determined for uploading tasks to satellites passing through the top attention area in a short time; after receiving the AIS signal of the target ship, the micro-nano satellite transmits the AIS signal to the ground; and S3, selecting to enter a regular imaging tracking mode under the condition of stable AIS signal receiving or a search imaging tracking mode under the condition of short-time AIS signal loss according to the AIS signal receiving condition, and carrying out relay tracking on the target ship. The method has the advantages that the remote sensing image data transmission quantity for carrying out the tasks and the time consumed for completing the tasks can be greatly compressed, and the requirements of high-timeliness tasks such as high-sea far-field ship escorting and marine ship searching and rescuing are met.

The invention relates to a robot path planning method and system based on optical vision. The robot path planning method comprises the following steps: acquiring an environment image of an environment where a robot is located and robot motion information; based on the environment image, a dense three-dimensional point cloud map is obtained through contrast enhancement, signal extraction, decoding and interpolation optimization; based on the dense three-dimensional point cloud map and the color RGB visible light environment image, generating a three-dimensional point cloud map with semantic tags through background elimination, image tracking, semantic recognition and clustering; estimating the motion state of the robot based on the three-dimensional point cloud map with the semantic tag and the motion information of the robot, carrying out global and local path planning, and obtaining a local path through track smoothing; and converting the local path into the actual action of the robot in combination with the motion state of the robot. According to the invention, through the collaborative design of the active optical visual perception architecture and multiple algorithms, the balance between cost control and performance improvement is effectively realized.

The invention relates to the technical field of image tracking algorithms, in particular to a target image tracking algorithm based on a Transform architecture. A template and an image of a search area are segmented into standard sizes and connected into a cascade vector, ViT is adopted as a backbone of a proposed framework, image pairs of a target and the search area are used as input, feature extraction and feature fusion are simultaneously carried out through a structured prior Transform network module, and feature extraction and feature fusion are carried out at the same time. A self-attention spatial prior generation network (SSP) is used for simulating spatial feature association, and a cross-attention-based Gaussian prior (CGP) and a random prior module (CRP) are proposed and are respectively used for establishing a spatial dependency relationship between feature marks and a matching relationship between a target and a search region. And finally, taking the output of the structured prior Transform network module as the input of the BBox prediction network to generate a tracking result. Therefore, better visual target tracking performance is realized.

The invention belongs to the technical field of intelligent traffic, particularly relates to a tunnel global tracking method based on an old camera re-identification technology, and aims to accurately realize global tracking and trajectory generation of a tunnel traffic target in a low-cost manner. The method comprises the steps of receiving an RSTP data stream, processing the RSTP data stream through a detection algorithm to obtain a sensing result, and feeding back the sensing result to the image tracking module. And processing a time sequence sensing result, adding vehicle speed information and a vehicle ID, and generating a tracking result including the vehicle ID, a target frame, a vehicle speed, a vehicle type, a color, a timestamp, a base station number and a camera number. On the basis of vehicle pixel picture data, upstream sample library candidates are matched for a current point position sample library target, if matching succeeds, the target inherits the global ID, if matching fails, the point position ID is reserved, and the target is output to a downstream point position and a simulation platform after the sample library is updated. And the trajectory generation post-processing module constructs a target matching relationship between adjacent camera sections through a frame insertion mode according to a feature re-identification result, and generates a tunnel global complete trajectory chain.

This application proposes a human body recognition system management method, apparatus, storage medium, and electronic device. First, the human body registration identity of the corresponding user is determined based on the target facial image. If no corresponding human body registration image exists for the registered identity, any target human body image from the target human body image tracking set is selected as the corresponding human body registration image. By automatically using any target human body image from the human body image tracking set as the human body registration image when no corresponding human body registration image exists, this overcomes the technical problem of requiring the user to actively provide a human body registration image, reduces user intervention, saves manpower and time, and is more convenient.

The present disclosure provides a method and device for tracking a moving object, a computer-readable medium, and an electronic device, and relates to the field of computer technology. The method comprises: obtaining an original video frame, which is acquired by a real camera with a fixed shooting angle; determining a moving object in the original video frame; creating a virtual camera based on the attribute parameters of the real camera and the position of the moving object in the original video frame; and cropping the original video frame based on the virtual camera to obtain a target video frame in which the image follows the movement of the moving object. The present disclosure can complete the image tracking of a moving object using only a real camera with a fixed shooting angle, achieving an automatic camera movement effect, and does not require manual intervention or the participation of a camera gimbal or motor, effectively reducing labor costs and hardware costs.

The invention discloses an adaptive multispectral target image tracking system and method. The system comprises an input layer, a processing layer and a transmission layer, the input layer is used for acquiring at least two paths of video signals; the processing layer is used for performing signal conversion, intelligent identification and fusion output on the video signals; and the transmission layer is used for respectively executing local video transmission and network RTSP stream distribution on the fused and output video signals. Decoding and format conversion are carried out on an access layer through a GS2971 / GM7150 chip, so that the RV1126 chip can receive different types of videos; in a processing layer, RKMedia is adopted to schedule video streams, YOLOv5 + NPU is adopted to perform intelligent identification, and a fusion module is adopted to perform picture enhancement; and in the output layer, low delay is ensured through local SDI transmission, and remote access is realized through RTSP flow. The system is compatible with complex scenes by adopting a layered decoupling thought.

An image tracking processing method, comprising: obtaining current frame point cloud data; preprocessing the current frame point cloud data to generate a projected image; obtaining a standard region image corresponding to standard frame point cloud data; calling a target tracking model, and obtaining a candidate region label corresponding to a candidate region based on the projected image and the standard region image; and determining a target tracking region corresponding to the current frame point cloud data according to the candidate region label.

The application provides an image tracking and collecting method, an imaging system and a computer device. The image tracking and collecting method comprises: initially collecting N pieces of image data of an imaging part, recording a position parameter of the imaging part in each piece of the image data, fitting a motion curve according to the position parameter, wherein N is greater than or equal to 3; predicting a predetermined position of the imaging part when collecting a next frame of the image data according to the motion curve; and controlling the movement of a ray generating device and a signal collecting device according to the predetermined position. Through the image tracking and collecting method, the system can predict the predetermined position of the imaging part when collecting the next frame of the image, and control the signal collecting device to move according to the predetermined position, so that the imaging part is always located in the signal receiving range of the signal collecting device. In the process of photographing the human body, human intervention is not required, the quality of the dynamic image is ensured, and the efficiency of the examination is improved.

The present embodiments provide systems and methods for tracking objects using refined bounding boxes and bounding box association. The system can include image sensors, a detector, a tracker, and a server.

The application provides an automatic delivery system of an interventional medical instrument based on image tracking, comprising: module M1: acquiring in-situ interventional medical instrument image information in real time, and acquiring instrument feature information based on the acquired interventional medical instrument image information; and module M2: delivering the interventional medical instrument based on the instrument feature information until the interventional medical instrument is delivered to a target position.

The present application relates to a human image tracking method and apparatus, and a non-volatile storage medium and an electronic device. The method comprises: acquiring a current image frame, and determining tracking information of the current image frame; determining a target face recognition region and a target head and shoulder recognition region from the current image frame; on the basis of a degree-of-overlap indicator between the target face recognition region and the target head and shoulder recognition region, and a first preset condition, respectively determining a first face recognition region and a first head and shoulder recognition region, and a second face recognition region and a second head and shoulder recognition region; acquiring a historical image frame corresponding to the current image frame; on the basis of a historical recognition region, determining a prediction region of a tracking object in the current image frame; matching a second region with the prediction region, so as to obtain a similarity indicator; and determining, from the second region, a target region with a similarity indicator between same and the prediction region meeting a second preset condition, and on the basis of the target region, updating tracking information of the prediction region.

Systems and methods for panoramic imaging in 2D and 3D ultrasound imaging are disclosed. The techniques disclosed herein use image registration to combine the 2D images into a 3D volume. Ultrasound images collected in different locations are combined using image tracking. When the transducer moves along a lateral direction (in line with a longitudinal axis of the transducer), a portion of the image is overlapped between frames and the overlapping frames can be patched. When the transducer moves along an elevational direction (non-parallel to the longitudinal axis of the transducer), there is no overlap between frames and the non-overlapping frames are not patched together but are stored side-by-side to create a volume. The storage and patching processes can be adaptive (e.g., the images are not stored or patched if there is minimal difference between neighboring frames).