224 results about "Point tracking" patented technology

The invention relates to the technical field of six-degree-of-freedom mechanical arm control, in particular to a six-degree-of-freedom mechanical arm trajectory tracking control method and system. The method comprises the following steps that an operation planning track sequence of the six-degree-of-freedom mechanical arm is obtained; executing real-time trajectory point tracking according to the operation planning trajectory sequence, and performing tail end attitude resolver analysis to generate tail end attitude resolver coupling data; when an end effector of the six-degree-of-freedom mechanical arm executes high-speed rotation load operation, end gyroscopic effect data are calculated; analyzing and estimating an uncontrolled offset vector according to the tail end attitude resolver coupling data and the tail end gyroscopic effect data; and dynamic offset vector correction processing is conducted on the estimated uncontrolled offset vector, so that intelligent control over operation of the six-degree-of-freedom mechanical arm is executed. According to the method, accurate tracking control over the tail end pose of the mechanical arm under the high-speed rotating load working condition is achieved through aerodynamic disturbance torque processing and rigidity weakest shaft recognition, and track deviation caused by composite disturbance is effectively compensated.

The invention is applicable to the technical field of medical image processing and mixed reality, and provides a laparoscopic surgery mixed reality navigation method based on deep learning and dynamic point tracking, which comprises the following steps: dynamically registering a three-dimensional model containing kidney, tumor and vessel with an initial frame of a laparoscope through a mixed reality alignment technology; the method comprises the following steps: constructing an operating forceps motion sensing model based on a time sequence deep neural network, realizing real-time control and parameter locking of a three-dimensional model pose, dynamically updating a two-dimensional feature point set by adopting a multi-feature-point combined tracker, constructing a candidate feature combination through a cross-quadrant sampling strategy, and constructing an operating forceps motion sensing model; a candidate feature combination is generated through a four-quadrant division and cross-regional sampling strategy, an optimal camera pose parameter is generated in combination with a re-projection error and pose continuity constraint, and an operation video is dynamically covered with a semitransparent three-dimensional model. The method can significantly enhance the spatial perception capability of the kidney anatomical structure, reduce the registration error of the kidney in the three-dimensional integrated kidney structure model and the laparoscope video, and improve the navigation precision.

The invention discloses a cross-language application program vulnerability mining method based on static analysis, which adopts a nested cross-programming language pointer analysis method and a micro-service cross-programming language taint tracking method to effectively solve the limitation of processing nested cross-language control flow and micro-service cross-language data flow. A nested language semantic boundary is accurately positioned by constructing a nested byte code, and a data stream is completely tracked by utilizing an interface relay technology, so that the detection precision is improved. According to the method, part of multi-end data streams and complex control streams of cross-programming language applications can be uniformly processed, and the analysis process in a cross-programming language environment is simplified; by nesting cross-programming language control flow diagram construction and double-end / multi-end data flow diagram construction, a unified analysis framework is constructed, seamless cooperation of static analysis among different languages is achieved, the analysis cost is reduced, the analysis efficiency is improved, efficient and accurate vulnerability mining is achieved in a complex cross-programming language application program, and the method is suitable for application and popularization. And the reliability of cross-language vulnerability detection is improved.

The invention discloses an optical imaging zoom point selection correction method and system, which can correct the deviation between a theoretical model and actual lens characteristics in real time through a peak point tracking algorithm and offset vector calculation in a second stage, effectively solve the problem of individualized errors caused by lens group assembly tolerance, material refractive index difference and the like, and improve the correction accuracy. The stratified sampling strategy of the first stage is combined with the slope interpolation of the third stage, untested points in a theoretical curve are filled, a high-precision focusing curve is generated, the full range of zooming is covered, and local focusing failure caused by traditional uniform sampling is avoided; according to the method, curve jitter caused by test noise or mechanical vibration is eliminated, a smooth zoom focusing curve is output, the anti-interference capability in actual control is improved, frequent jitter of a focusing motor is avoided, a search strategy is dynamically adjusted in combination with definition data, an actual peak point is quickly locked, and the problem of overmodulation or slow convergence caused by a traditional fixed step length is solved.

The invention belongs to the field of power distribution network monitoring, and provides a power distribution box damage behavior monitoring method and system based on human body posture estimation, and the method comprises the steps: obtaining a monitoring video of a power distribution box terminal for preprocessing, and obtaining a preprocessed video frame; based on the preprocessed video frame, performing posture recognition by using a pre-trained human body and distribution box posture recognition model to obtain a human body and distribution box posture recognition result; key point tracking and identity association are performed by using a human body and distribution box posture recognition result, a space-time skeleton diagram is constructed, and the space-time skeleton diagram is screened to generate a skeleton space-time sequence; on the basis of the skeleton space-time sequence, a pre-trained space-time diagram convolutional network model is utilized to identify a damage behavior, and a damage behavior identification result is obtained; when the confidence coefficient in the damage behavior recognition result exceeds an alarm threshold value, alarm information is generated, and an alarm process and safety linkage control are triggered. According to the invention, accurate monitoring and real-time early warning of the damage behavior of the distribution box are realized.

The invention discloses a virtual hand grabbing method and system for mixed reality interaction, which is based on a hinge proxy hand model, utilizes a hand tracking device and a physical engine, and fuses hand key point tracking data and a force closing algorithm, and is a grabbing method which is based on physical simulation, is real-time and efficient and does not need to predefine object information. High-precision and real-time hand-object interaction is realized, a virtual hand observed by a user in a mixed reality picture is effectively prevented from penetrating through a virtual object, and the interaction experience in VR / AR equipment is remarkably improved. Moreover, the method supports the real-time adjustment of the dynamic grabbing gesture based on the surface of the object, and is suitable for various VR / AR devices.

The invention provides a human body posture abnormity assessment method based on a body measurement all-in-one machine, and the method comprises the steps: obtaining the video data, collected by multiple cameras, of the bending and foot touching actions of a subject, carrying out the preliminary positioning of a spine, a lumbar vertebra and thoracic vertebra transition region, and key points of legs, and extracting the coordinates of the key points; leg joint angle change is calculated according to the leg key point coordinates, if the angle change amplitude is lower than a preset threshold value, it is judged that leg flexibility is insufficient, a key point tracking strategy is adjusted, and the tracking density of a leg area is increased; according to the posture abnormity score, generating a posture abnormity report of the bending and foot touching actions of the subject, and marking specific positions of spine curvature abnormity, stiffness increase of a transition area between the lumbar vertebra and the thoracic vertebra and insufficient leg flexibility; and dynamically updating a key point tracking strategy and an attitude anomaly judgment threshold according to the action normativity score, and optimizing the accuracy and real-time performance of subsequent action evaluation.

The invention discloses a civil engineering foundation pit deformation monitoring system based on image analysis, and relates to the technical field of foundation pit deformation monitoring, and the system collects and enhances a multi-modal image, eliminates interference, corrects image distortion, tracks key points, constructs a panoramic image, generates a point cloud, extracts deformation features, and constructs a deformation map. The method comprises the following steps: identifying a construction disturbance event, constructing a deformation chain and performing causal reasoning, identifying a high-risk area, predicting a deformation trend and outputting an early warning, displaying a monitoring result, collecting user feedback and synchronizing system data. A deformation track is accurately extracted through image correction and key point tracking, a three-dimensional deformation map is constructed in combination with point cloud modeling, causal analysis is carried out based on construction disturbance and a deformation path, a high-risk area is predicted and identified by using a time sequence trend, an early warning is given out, user feedback collection and result synchronization are supported, and monitoring intelligence and response efficiency are improved.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention provides a video point trajectory constraint-based video description method and system. The method comprises the following steps of: sampling a key frame image and acquiring a space-time trajectory of continuous inter-frame pixel points by using a point tracking algorithm; performing average pooling operation on the visual features of the corresponding frames of the same track fragment; performing semantic alignment on the text features, the visual features and the track features and then performing multi-head attention feature fusion; semantic correlation score calculation is carried out on the visual areas corresponding to the track fragments, correlation scores are arranged in a descending order, the correlation scores are accumulated, and a threshold value is set; jointly optimizing a video point tracking model by using language generation loss and focusing loss; and decoding the multi-source features after focusing optimization to obtain a final video description result. The method introduces a video point trajectory aggregation strategy, explicitly models the dynamic characteristics of a target in a space-time dimension, retains the spatial appearance and time coherence of an object, and effectively solves the problems of semantic fracture and description fragmentation in a complex scene.

The invention provides an unmanned aerial vehicle cluster fire point real-time detection and positioning method and system. The method comprises the following steps: acquiring a visible image and an infrared image synchronously shot by a single unmanned aerial vehicle, and recorded shooting time and unmanned aerial vehicle pose data; processing the visible image and the infrared image based on threshold segmentation and morphological filtering to obtain a moment image pair; inputting the moment image pair into the trained double-attention segmentation network, and outputting a fire semantic map; analyzing and processing the fire semantic map based on the connected domain, outputting candidate fire points, and obtaining initial geographic coordinates of the candidate fire points based on the pose data of the unmanned aerial vehicle; based on the shooting time, tracking the initial geographic coordinates of the candidate fire points in real time through Kalman filtering, and outputting a fire point tracking and positioning result of the single unmanned aerial vehicle at the to-be-positioned moment through dynamic compensation of elevation data; and fusing the fire point tracking and positioning results of all the unmanned aerial vehicles in the unmanned aerial vehicle cluster at the to-be-positioned moment to obtain a fire point fusion positioning result at the to-be-positioned moment.

The invention provides an outdoor cross-modal robust positioning and navigation method for extreme severe weather, and relates to the field of online positioning and navigation of mobile robots. The method comprises the following steps: acquiring and preprocessing data; generating a path point set based on a CMR network, perfecting the path point set through local trajectory fitting and error optimization, and constructing a path point relative pose measurement-topology hybrid map; motion prior estimation is carried out based on Doppler velocity, effective matching prior estimation is generated in combination with LiDAR odometer information, cross-modal pose estimation is carried out through a CMR network, and a pure tracking strategy and PD controller path point tracking are adopted; and geometric and intensity alignment is carried out on the forward 4D millimeter wave radar point cloud and the omnidirectional 3D laser radar point cloud through a CMR network, a rotation matrix and a translation vector are output, and accurate positioning is completed. According to the invention, the sensing and positioning defects in extreme weather in the prior art are overcome, and the positioning precision and robustness are improved.

The invention relates to the technical field of 3D visual inspection, in particular to a dynamic shielding area multi-view time sequence point cloud completion method and system in 3D visual inspection. The method comprises the following steps: collecting a multi-view time sequence image of a dynamic detection object for providing a data basis for feature point tracking; and carrying out Lucas-Kanade optical flow method tracking on the feature points, associating the feature points of different visual angles, describing a motion track of the feature points through an optical flow vector, carrying out cross-visual-angle and cross-time feature point matching, and establishing time sequence feature association to obtain time sequence feature points. According to the dynamic occlusion area multi-view time sequence point cloud completion method in 3D visual detection, 360-degree time sequence data of a dynamic detection object is acquired through multi-view time sequence image acquisition, and comprehensive basic data support is provided for subsequent feature tracking.

The invention discloses a monocular vision inertial fusion positioning method based on an optical flow smoothness constraint, which comprises the following steps: designing a visual feature enhancement algorithm based on the optical flow smoothness constraint, improving feature point tracking and elimination in a visual inertial navigation system, and carrying out tight coupling optimization on a visual residual error part so as to improve the quality of observed quantity in positioning; comprising the following steps: generating a clustering result and a support factor for each feature point tracking vector of an acquired image through optical flow smoothness constraint; constructing a compound expectation maximization (EM) algorithm, fusing a support factor into algorithm solution to serve as prior information, and eliminating wrong matching point pairs to obtain a correct matching vector; adaptively adjusting the visual weight of the pose estimator, and performing region shielding; and monocular vision inertial fusion positioning based on optical flow smoothness constraint is realized.

The invention discloses a method for generating a high-precision dynamic digital human, which belongs to the field of computer vision and comprises the following steps of: extracting skeleton motion and frame-level potential codes from a multi-view video, driving a coarse model network to generate a three-dimensional grid, and realizing attitude alignment by combining double quaternion skin and embedded deformation; a vertex three-dimensional corresponding relation is obtained through 2D point tracking and a depth map, and grid alignment precision is optimized; on the basis, animatable Gaussian textures are trained, and texture detail modeling is achieved; a physical solver is further introduced, the physical response of cloth is solved according to the bone posture and the material type, and die penetration and distortion are eliminated; meanwhile, modeling is carried out on texture offset by adopting a space-time Transform, so that the dynamic consistency is improved; and finally, high-fidelity image rendering is completed through analytical sputtering, and a dynamic digital human which is rich in details, stable and controllable is generated.

The invention relates to the technical field of AI evaluation, in particular to a post competency AI evaluation system and a post competency AI evaluation method, the post competency AI evaluation system comprises an intelligent scanning layer, a diagnosis decision-making layer, a talent development prediction layer, an education optimization layer and a data closed-loop feedback module, and the intelligent scanning layer comprises a multi-modal biological characteristic acquisition module and a dynamic ability modeling module; the talent development prediction layer comprises a capability-post matching engine and a demission risk early warning unit; the education optimization layer comprises a course reform simulator and a virtual teaching and research room; compared with the defects that in the prior art, single questionnaire or static video analysis is relied on, the evaluation dimension is one-sided, and subjective interference is likely to happen, the scheme synchronously integrates three types of biological characteristics of facial micro-expression (52 key point tracking), writing force waveform (1000 Hz pressure sense) and VR situation response (1200 + scene library), and the evaluation accuracy is improved. Holographic dynamic capture of a candidate cognitive mode, anti-pressure ability and actual combat response is realized, and objectivity and ecological validity of competency evaluation are remarkably improved.

The invention discloses a cardiac ultrasound key point tracking implementation method based on a pseudo tag, and relates to an ultrasound image analysis technology. The method comprises the steps of extracting a key frame from an ultrasonic image set corresponding to a cardiac pulse period, extracting a key point on the key frame as a pseudo tag, and performing key point detection on each intermediate frame based on a preset tag propagation model according to the pseudo tag and in combination with a time sequence relationship between ultrasonic images in the ultrasonic image set to obtain a key point detection result; key points, corresponding to the key pseudo labels, on each middle frame are extracted to serve as middle pseudo labels; and performing optimization training on the propagation model according to the intermediate pseudo-tag to obtain an optimized tag propagation model. According to the method, the high-quality pseudo label can be generated online, the pseudo label of the middle frame is generated through the propagation model in combination with the high-quality pseudo label, and the potential value of unlabeled data is fully mined; and finally, a propagation model is further optimized and trained by using the generated intermediate frame pseudo tag, and the efficiency and the intelligent degree of cardiac ultrasound key point tracking are improved.

The invention provides a robot path planning method and system based on path position point-by-point tracking, and belongs to the technical field of robot path planning. Comprising the steps of performing safe spatial domain coverage on an obstacle, and generating an intermediate node; searching the end point by adopting a node drop point detection strategy and an intermediate node search strategy to obtain a path connecting the starting point and the end point; deleting redundant points from the path to generate an initial path; segmenting the initial path to obtain a position coordinate point set; and point-by-point tracking is carried out on the coordinate points by adopting an APF algorithm so as to obtain a final path. According to the method, the problems of high randomness, low sampling efficiency, node redundancy and the like in the robot path planning process can be solved, and the calculation efficiency and the environment adaptability in the robot path planning process are considered.

The invention discloses an ultrahigh-speed imaging method, device and system of an event camera, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a superpixel segmentation mask of a motion area according to an event trigger position and an image appearance-position feature, selecting event points in the superpixel segmentation area for continuous time point tracking, and calculating an optical flow field with continuous time and dense space by combining a point tracking time sequence track and the spatial superpixel mask. An imaging result of any to-be-imaged moment t between the two frames is calculated by using the optical flow field, and high-precision imaging of an ultra-high-speed motion scene can be realized.

The invention discloses an unmanned aerial vehicle detection and tracking adaptive low-altitude economic road picketing and logistics method, and relates to the technical field of unmanned aerial vehicles. In the aspect of road picketing, road vehicle videos are collected through a combined sensor system carried by an unmanned aerial vehicle, and a driver behavior danger coefficient and a driving danger coefficient are calculated through information processing; vehicle danger levels are evaluated based on the two and corresponding management and control measures are taken, such as conventional monitoring, key tracking, emergency early warning and the like of different levels. In the aspect of logistics application, order receiving and processing, path planning and unmanned aerial vehicle scheduling, flight distribution and goods signing and returning are included, and specific operation and judgment methods of all links of logistics are covered.

A video processing method and a terminal are provided. In the method, when the terminal records a video, the terminal may shoot (acquire) an image by using a camera (the image shot by using the camera is referred to as an original image below), and determine a focus of the shooting based on the original image. Then, the terminal may implement image focus tracking on a photographed object displayed in a first image region in which the focus is located, and implement audio focus tracking on a photographed object displayed in a second image region in which the focus is located. A focus tracking video is obtained through the image focus tracking and the audio focus tracking.

The invention discloses a zero point tracking method, device and equipment of a gas ultrasonic flowmeter and a medium. The method comprises the following steps: determining a feature vector of a current gas flow measurement value according to a measurement correlation parameter corresponding to the current gas flow measurement value; the measurement correlation parameters comprise signal characteristic parameters, environment parameters and signal statistical parameters; classifying the feature vectors through a drift detection model, and determining vector categories of the feature vectors; the vector category is a zero drift feature vector or a normal feature vector; and when it is determined that the vector category is a zero drift feature vector, a zero correction value is determined through a drift calibration model, and the current gas flow measurement value is corrected according to the zero correction value. According to the embodiment of the invention, zero point tracking can be quickly and accurately carried out on the gas ultrasonic flowmeter arranged in the gas pipeline based on the measurement correlation parameters, the drift detection model and the drift calibration model, the time cost and the labor cost are reduced, and the accuracy is improved.

The application discloses a three-dimensional multi-extended target tracking method based on a target-to-target reference deviation state, which determines a reference target, uses the deviation between the reference target and an extended target to track the extended target, and further obtains the track of the extended target, so that the posture and contour of the multi-extended target in a three-dimensional space can be accurately estimated; meanwhile, the multi-extended target is tracked based on the reference target, the posture and contour state of the extended target can be accurately estimated during the occlusion of the extended target, and the problem of tracking loss after the occlusion of the extended target is effectively solved. In addition, the state vector of the reference target is used to establish the state vector of all the extended targets, and the speeds of all the extended targets in the application are different and change with time, so that the application has a wider application scenario compared with the traditional method of tracking all contour points through the extended target particles.

The invention discloses a target self-adaptive positioning method and system for low-altitude routing inspection, and aims to solve the problems that target positioning depends on additional hardware, the complex environment adaptability is poor and the real-time performance is insufficient in the existing low-altitude routing inspection. According to the method, an unmanned aerial vehicle collects a target object image and extracts pixel coordinates, Beidou positioning data and IMU attitude information are fused, and normalized coordinates in a camera coordinate system are obtained through coordinate conversion; a dynamic virtual baseline technology is innovatively introduced, depth information of a target object is obtained through feature point tracking, motion solution and depth calculation, and finally three-dimensional longitude and latitude positioning of the target object is achieved by combining a rotation matrix and a coordinate conversion algorithm. The method does not need extra hardware, improves the positioning precision and real-time performance in a complex scene, and is suitable for the dynamic target positioning requirement in the field of low-altitude inspection.

This paper describes a method and system for determining the 3D location of objects within an identified portion of an image. The image processing system receives an image and identifiers of locations within the image. The image can be input into a machine learning model to detect one or more objects within the identified locations. Multiple images can then be used to generate location estimates for those objects. Based on the location estimates, accurate 3D locations can be calculated.

The present invention discloses a multifunctional automated spatial point tracking and measurement method and device, which relates to the field of tracking and measurement technology, including: obtaining N monitoring points for priority sorting, obtaining monitoring point sequence information, presetting the monitoring acquisition frequency according to the monitoring point sequence information, and generating monitoring area information; a built-in high-precision navigation module automatically navigates to the monitoring area information, fixes the target icon on the target monitoring point, and obtains the target monitoring image information of the monitoring area information; trains and constructs a point tracking module, performs target positioning and coordinate extraction, and determines the target pixel coordinate information; performs target coordinate calculation to obtain the target's actual spatial coordinates, and stores the target's actual spatial coordinates in sequence to generate N spatial point measurement results. The present invention solves the technical problems in the prior art of difficult to guarantee monitoring frequency, low measurement efficiency, and insufficient positioning accuracy, and achieves the technical effect of improving the efficiency and accuracy of spatial point tracking measurement.

The present invention discloses a video tracking method, device, terminal, and medium. The present invention combines the semantic features of the target tracking point itself with the surrounding contextual features, improving the ability to query spatial information. This allows the correct spatial features of the target tracking point to be found even in long videos or when the tracking target undergoes significant changes. Point tracking tasks in long videos are accomplished by forming a point query based on the semantic features, contextual features, and point positions of the target tracking point, and updating the point query frame by frame. The method is applicable to fields such as video editing, augmented reality, 3D reconstruction, and optical flow estimation.

Systems and methods are described for performing a clinical procedure utilizing sentiment analysis. The system includes an imaging system and a control system. The system is configured to obtain image data of a tissue region of a patient from the imaging system; obtain input data indicative of an assessment to perform on the tissue region; analyze the input data and the obtained image data to select one or more target points in the obtained image data to track for performing the indicated assessment; track, via a point tracking model and across image data obtained via a second imaging modality of the imaging system, the one or more target points for an assessment period to determine one or more tissue parameter values for each tracked target point; provide an output to a user based on (i) the input data, and (ii) the one or more tissue parameter values.

This paper proposes a video description method and system based on video point trajectory constraints. The method includes: sampling keyframe images and using a point tracking algorithm to obtain the spatiotemporal trajectories of pixel points between consecutive frames; performing an average pooling operation on the visual features of frames corresponding to the same trajectory segment; semantically aligning text features, visual features, and trajectory features before performing multi-head attention feature fusion; calculating semantic relevance scores for the visual areas corresponding to the trajectory segments and sorting them in descending order by relevance score, accumulating the relevance scores and setting a threshold; jointly optimizing the video point tracking model using a language generation loss and a focus loss; and decoding the multi-source features after focus optimization to obtain the final video description result. By introducing a video point trajectory aggregation strategy, the present invention explicitly models the dynamic characteristics of the target in the spatiotemporal dimension, preserving the spatial appearance and temporal coherence of the object, and effectively solving the problems of semantic discontinuity and description fragmentation in complex scenes.