70 results about "Motion estimation" patented technology

The invention relates to the technical field of machine learning, and particularly discloses an intelligent parking guidance and reverse vehicle searching system based on multi-source heterogeneous data fusion. The system comprises a data perception fusion layer, a dynamic prediction decision-making layer and a user service interaction layer, realizes multi-step advanced probability prediction and dynamic optimal path planning of a parking space state through fusion of geomagnetic detection, video identification, payment flow, traffic situation and activity information, and realizes the optimal path planning of the parking space state based on multi-mode induction and live-action AR reverse vehicle searching guidance. And the parking efficiency and the user experience are improved.

The invention belongs to the technical field of visual angle follow-up vehicle panoramic look-around, and particularly relates to a visual angle follow-up vehicle panoramic look-around system based on dynamic prediction, which is characterized in that monocular vision and laser radar time sequence data are fused, and a dense and stable dynamic target depth sequence is generated through dynamic target initial perception, cross-modal time sequence alignment and inter-frame depth correlation modeling; through environment interaction modeling and target behavior intention prediction double-branch parallel processing, a dynamic self-adaptive accurate track is output in combination with a weighted fusion strategy, and the prediction weight dynamic adjustment requirement of a complex motion scene is adapted; after a virtual view angle image is preliminarily synthesized based on the predicted trajectory, deviation detection and coordinate dynamic correction are realized through inter-frame target consistency verification, and artifact area positioning and pixel-level restoration are synchronously completed; multi-modal preprocessing and algorithm tasks are deployed according to hardware, and a model lightweight optimization and inter-frame prediction result multiplexing strategy is combined, so that low-delay operation of the system in a high-resolution panoramic image output scene is ensured.

The invention relates to the technical field of intelligent equipment scheduling planning, and discloses a scene scheduling method and system for intelligent equipment, and the method comprises the steps: determining the task emergency degree and the motion complexity based on a dual-domain scheduling factor construction mechanism; constructing a scheduling priority weight; screening a conflict node set; generating an avoidance decision by adopting a double-case evaluation mechanism; and finally, synchronously issuing the avoidance decision to related intelligent equipment. In the prior art, there is no scheduling method of a priority guidance and fine conflict judgment mechanism, and especially in a dense operation scene of multiple mobile devices including an industrial AGV, a luggage transfer robot and a mobile charging vehicle, dynamic prediction and active avoidance of conflict risks are difficult to realize. Due to the fact that task and motion double-domain factor construction and a space and time double-condition conflict recognition mechanism are introduced, active avoiding and real-time response of device-level scheduling are achieved, and the cooperative operation stability of intelligent devices is improved.

The invention discloses a three-dimensional motion estimation method based on an event camera. The method comprises the following steps: 1, constructing a time-space decoupling event time sequence and spatial representation; 2, geometric structure and context features are extracted through a space encoder, and time sequence evolution features are extracted through a time sequence encoder; 3, respectively constructing a spatial correlation graph and a time sequence correlation graph based on the geometric structure characteristics and the time sequence evolution characteristics; 4, constructing a pixel track curve according to the event density; 5, geometric structure and time sequence evolution characteristics are sampled along the pixel track curve, and a space-time correlation graph is generated; 6, iteratively optimizing pixel track curve parameters according to the space-time correlation graph and the context features; and 7, utilizing the optimized pixel track curve and the derivative thereof to accurately predict the three-dimensional motion. The method obviously improves the three-dimensional motion estimation precision of the event camera in a complex scene, is suitable for the fields of automatic driving and robots, and has a good engineering application value.

The invention provides an HRRP target micro-motion characteristic extraction method based on chaos optimization and polarization synthesis, and aims at the field of high-resolution one-dimensional range profiles. The method comprises the following steps: performing optimal polarization contrast enhancement and optimal polarization fusion based on a maximum signal-to-noise ratio criterion on HRRP multi-channel polarization data; performing period estimation on the polarization fusion data in combination with second harmonic back-off optimization and autocorrelation / amplitude difference enhancement processing; chaotic mapping and back propagation are combined to avoid the problem that traditional optimization is prone to falling into local minimum, and precession angle and size results of the space target are obtained. According to the method, the feature contrast of HRRP data is remarkably improved through polarization fusion, meanwhile, high-precision estimation of the micro-motion features of the space target is achieved under the condition of a low signal-to-noise ratio, the stability of periodic detection within limited observation time and the global search capability of parameter inversion are effectively improved, and the method is suitable for large-scale popularization and application. And the accuracy and robustness of space target micro-motion estimation in a complex environment are obviously enhanced.

The invention discloses a water conservancy and hydropower potential safety hazard intelligent identification method and system based on image identification, and relates to the technical field of water conservancy and hydropower engineering management. The method comprises the following steps: acquiring a real-time image data sequence and an engineering facility digital twinborn model; performing space mapping and timestamp alignment on each frame of image and the digital twin model to generate a registered time-space image data set; layered feature extraction is carried out on the registered time-space image data set, and structured engineering defect features are analyzed from potential abnormal targets in combination with three-dimensional geometric structure information of a digital twin model; performing association analysis on the engineering defect features and engineering attribute information and historical operation and maintenance data of the digital twin model to judge hidden danger types of the engineering defects, and calculating risk levels of the engineering defects according to a preset risk assessment matrix; and generating a structured potential safety hazard report. The method realizes high-precision context sensing of potential safety hazards of water conservancy and hydropower and has dynamic prediction capability.

The invention relates to the field of intelligent medical treatment, in particular to a dynamic illumination prediction method and device based on a refraction state and a program product. Comprising the steps of obtaining eye refraction data of a to-be-tested person; carrying out clustering and feature fusion on the refractive data to obtain fusion features; outputting the fusion features to a trained prediction model for prediction to obtain a brightness prediction value and a color temperature prediction value; and the prediction model carries out weight parameter initialization by compressing an abnormal value of refractive data and compensating distribution deviation of refractive clustering. According to the method, personalized expression of different myopia degrees on illumination perception differences can be achieved, brightness and color temperature parameters are dynamically predicted, actual lighting equipment is driven, dynamic balance of visual comfort and environmental adaptability is achieved, and the method has good actual application value.

A system including an internal sensor configured to generate first sensor data, an external perception sensor configured to generate second sensor data, at least one memory configured to store computer executable instructions, and at least one processor is disclosed. The at least one processor is coupled to the internal sensor, the external perception sensor, and the at least one memory. The at least one processor is configured to execute the computer executable instructions to: (i) compute a first velocity output based upon the first sensor data; (ii) compute a second velocity output based upon the second sensor data; (iii) compute a relative velocity between the first velocity output and the second velocity output; (iv) determine the relative velocity is outside a predetermined threshold value; and (v) perform an intervening action in response to the determining.

The application belongs to the technical field of image recognition, and provides a target tracking method and system based on AI vision, which comprises the following steps: original video frame acquisition; environment adaptive image enhancement; multi-target detection and multi-modal feature extraction; local optical flow motion estimation; multi-target trajectory association; occlusion processing and re-identification; trajectory output and result analysis; the method sets a physical-depth learning cascade defogging model, dynamically switches light / thick fog processing paths through dark channel mean, fuses atmospheric scattering physical priori and U-Net residual correction, has an environment adaptive perception architecture, breaks through the failure bottleneck of traditional single model under sudden fog concentration, constructs a trajectory cognition system coupled with appearance-motion-geometry, designs a dynamic cost matrix and a feature cache pool, solves the ID switching problem caused by similar target aggregation and long-time occlusion, and improves the accuracy of target tracking in an occlusion environment.

The invention discloses a photoelectric turret visual axis stability control method based on self-adaptive disturbance prediction compensation and a computer product, and the method comprises the following steps: S1, establishing a gyro speed loop discrete model, calculating the deviation between the output of the model and the angular velocity of a visual axis measured by a gyro, and obtaining a disturbance estimation value through an inverse model and a low-pass filter; s2, constructing a mathematical model of disturbance based on a recursive least square method, and performing multi-step forward prediction on equivalent disturbance by using the model; and S3, carrying out weighted fusion on the predicted equivalent disturbance and the real-time disturbance estimation value, and finally feeding a fused compensation signal forward to the input end of the control system to counteract the influence of the actual disturbance on the system. According to the method, real-time estimation and dynamic prediction are fused, the learning-prediction capacity is added on the basis of perception-compensation of a traditional DOB, the method is suitable for a photoelectric turret control system which is remarkable in periodic disturbance or sensitive to delay, phase lag of the traditional DOB is reduced, and the stable control bandwidth and disturbance rejection capacity of a visual axis are greatly improved.

Various embodiments include techniques for encoding media frames using a multistage search without the processing overhead of pyramidal motion estimation techniques. The first stage of the multistage search generates many motion vectors, where each motion vector is based on a small range full pixel search of a pixel group in the media frame. The video encoder selects a full pixel best motion (FBM) vector for all pixel groups from the motion vectors generated during the first stage. The second and subsequent phases of the multistage search is based on the FBM vector from the prior stage as a starting point, where the search range for each subsequent phase is larger than the search range of the prior stage. The multistage search can be performed over a fixed number of stages. Alternatively, the multistage search can be terminated when the cost value for the current stage is below a threshold value.

The invention provides a science and technology project performance dynamic prediction method and system combining digital twinning and artificial intelligence, and relates to the technical field of science and technology project management data analysis. According to the method, a performance indicator system and project process data are obtained, a mapping relation between indicator calibers and process data is established, a project digital twinborn body containing task dependence, milestone and resource boundary constraints is constructed, and a feature sequence with consistent constraints is generated; on the basis, a consistency constraint training performance prediction model is introduced, rolling prediction and confidence evaluation in the operation period are achieved, calibration of the model or the mapping relation is triggered when prediction credibility is insufficient or management constraint is violated, and therefore the caliber consistency, constraint rationality and trend availability of a performance prediction result are improved.

The invention discloses a synchronous control system for multi-channel small lamp sequential light emitting and three-line array camera synchronous shooting and a time delay compensation method thereof, and the system comprises a high-performance control module, a time delay detection module, a small lamp driving module, a camera triggering module, a parameter storage module and a man-machine interaction module. According to the time delay compensation method, multi-scale delay modeling, dynamic prediction and layered closed-loop control principles are combined, a real-time time delay characteristic function is established by using exponential weighted filtering and an autoregression prediction model, error self-correction is realized through proportional-integral adaptive control, and an outlier rejection mechanism is introduced to suppress noise interference. The system can be applied to scenes such as industrial visual detection, scientific experiment high-speed imaging, medical visual acquisition and motion analysis, the problem of synchronization errors caused by delay drift and signal coupling in a multi-channel system is effectively solved, stable synchronization of the system under the high-frequency triggering condition is achieved, and the errors are controlled within + / -5 microseconds.

The application relates to the technical field of satellite meteorological prediction, and discloses an optical flow-depth learning satellite multi-channel data extrapolation method. The method comprises the following steps: acquiring and preprocessing time-series satellite multi-channel data; a two-dimensional motion field is estimated by using a variational optical flow algorithm, preliminary space-time extrapolation is carried out in combination with a semi-Lagrangian algorithm and a mass conservation constraint; a deep learning model integrating space-time convolution and a recurrent neural network is constructed, and the preliminary extrapolation result and original deep features are jointly trained and predicted; two types of results are dynamically fused through an adaptive weight fusion mechanism; finally, the fusion result is corrected based on atmospheric dynamics and radiation transmission physical constraints, and a physically consistent multi-channel extrapolation field is generated. The method combines the advantages of physical motion estimation and data-driven learning, and improves the accuracy and rationality of satellite observation extrapolation from the minute level to the hour level.

The application provides an online matching optimization method combining geometry and texture and a three-dimensional scanning system, and the method comprises the following steps: acquiring a depth texture image of a target object by using a three-dimensional scanning device; obtaining a preliminary pose estimation of the three-dimensional scanning device according to the depth texture image information; and performing optimization on the preliminary pose based on the depth geometry information and the texture information of the image to obtain a refined inter-frame motion estimation. The method fuses geometric and optical double constraints, fully utilizes texture information, proposes to calculate and solve texture images, obtains feature values which are not sensitive to light and have strong anti-interference ability to replace unprocessed pixel intensity, so that the adaptability of the system to optical changes is stronger, the registration result is more robust, and the inter-frame matching efficiency and accuracy are improved.

The application provides an audio noise reduction method and a monitoring device, and relates to the technical field of monitoring, wherein the method comprises the following steps: acquiring video data and audio data of at least one direction in a current time period; for each direction, determining motion information of each sound source target in the current time period based on the video data of the direction, and determining spectral change information of the current time period relative to a previous time period based on the audio data of the direction; based on the spectral change information and the motion information of each sound source target in the current time period, predicting a noise reduction frequency band in a next time period; and reducing noise of each noise reduction frequency band in target audio data in the next time period. The application can realize the association of the sound source target and the spectral change information based on the spectral change information and the motion information of each sound source target in the current time period, and can further dynamically predict the noise reduction frequency band in the next time period, so as to realize the dynamic noise reduction of the audio data.

Smoke detection device (1) for detecting smoke (10) from a fire (9) in a monitored area (U), with a camera interface (5) for receiving a sequence of images (3) with at least two temporally successive individual images (4), wherein the individual images (4) show the monitored area (U), with a difference module (6) for eliminating a scene background (8) of the monitoring area (U) in the individual frames (4) and for generating background-cleaned intermediate frames (13), with an evaluation module (14), wherein the evaluation module (14) is designed to perform a motion estimation to determine a movement of smoke sections of the smoke (10) in the monitoring area (U) based on the background-cleaned intermediate images (13), characterized by a background module (11), wherein the background module (11) is configured to create a background model (12) of the monitoring area (U), wherein the difference module (6) is configured to apply the background model (12) to the individual images (4) to generate the background-cleaned intermediate images (13), wherein the application of the background model (12) to the individual images (4) is configured as a division of the individual image (4) by the background model (12).

The invention relates to the technical field of machine vision and quality detection, and discloses a sponge grinding block surface defect detection system based on AI vision. The system comprises a virtual mapping modeling module, a visual attribute flow injection module, a defect evolution rehearsal module, a directional detection beam generation module and a model feedback correction module. According to the technical scheme, a virtual mapping model synchronous with a production line is constructed, image data is converted into a visual attribute flow to drive model evolution, and the defect initiation and diffusion process is rehearsed in a virtual space; generating a directional detection wave beam of a physical space according to a rehearsal result, and accurately locking the position and the contour of the defect; and a feedback data reverse correction model is utilized to form a detection and updating closed loop. According to the method, the conversion from passive identification to dynamic prediction is realized, and the perspectiveness, the accuracy and the system adaptability of detection are improved.

The present application relates to the technical field of grain processing process control, and discloses a rice milling control method and system based on rice processing precision, aiming to solve the problems of detection lag, poor control precision and inability to adapt to the processing requirements of new batches of brown rice in the prior art, and mainly comprising the following steps: collecting rice milling dynamic images through an industrial camera and extracting multi-dimensional visual features; extracting brown rice initial features before milling to retrieve database to obtain reference parameters; dividing the milling stage according to dynamic timing features and adjusting the sampling frequency; fusing the features through cross-attention and mapping the visual milling index into the processing precision through a timing network; and dynamically predicting the remaining time based on the visual index and the reference parameters, and automatically stopping when the deviation and time conditions are met. The present application realizes in-situ dynamic visual perception and closed-loop adaptive control of the rice milling process, solves the cold start problem in the batch independent scene, and significantly improves the consistency and control efficiency of the rice milling precision.

The invention discloses an indirect measurement method and system based on geometric model calculation, and belongs to the technical field of measurement, and the method comprises the steps: obtaining a plurality of view angle image data of a target region, and constructing an initial geometric model based on the plurality of view angle image data; acquiring inertial measurement data, and driving the initial geometric model by using the inertial measurement data to generate a dynamic prediction model; acquiring real-time image data, and comparing the real-time image data with the dynamic prediction model in a geometric feature level to generate deviation data; correcting an error of the inertial measurement data based on the deviation data to generate corrected inertial measurement data; and updating the dynamic prediction model by using the corrected inertial measurement data, and calculating a measurement result based on the updated dynamic prediction model. According to the method, geometric model construction and inertial measurement data fusion are adopted, and a real-time image feedback correction mechanism is combined, so that high-precision dynamic indirect measurement can be realized.

This invention discloses a robust optimization intelligent meeting scheduling method for MCU video conferencing systems, comprising the following steps: acquiring multi-source data, including meeting metadata, real-time status data, and time-series correlated status data; identifying meeting characteristics based on the multi-source data, and dividing the meeting into different time segments according to the meeting progress, and labeling each time segment with a meeting characteristic identifier; dynamically predicting the establishment and release times of multimedia channels in cross-regional multi-venue video conferencing based on the meeting characteristic identifiers; constructing a global resource robustness optimization model, aiming to maximize the global resource meeting guarantee efficiency, and generating a resource scheduling strategy; and adaptively executing the allocation of resources on the venue side and the delay release control of multimedia session channels according to the resource scheduling strategy. This invention achieves adaptive allocation and execution control of resources on both the venue side and the central side, which can greatly improve the service efficiency of MCU video conferencing systems.

The invention discloses an intelligent dinner plate scheduling and pricing method, a medium and equipment, and the method comprises the steps: receiving multi-source sensing data which comprises the unique identification information of dinner plates, the image data of a meal taking region and the weight change data of the dinner plates, and carrying out the multi-modal fusion recognition, so as to generate binding pricing information; on the basis of the bound pricing information and historical consumption data, dynamic meal preparation suggestions and supplementary scheduling instructions are generated through a space-time prediction model, and then corresponding interfaces are called to execute resource allocation and collect dinner plate recovery state data; and finally, performing residue visual analysis on the dinner plate recovery state data to generate a waste report and outputting the waste report. According to the invention, closed-loop intelligent management from consumption perception and intelligent pricing to production resource dynamic prediction and dinner plate real-time scheduling is realized, the operation efficiency is improved, and resource waste is reduced.

The invention provides a video frame insertion quality detection method and system, electronic equipment and a storage medium, and relates to the technical field of video detection, and the method comprises the steps: respectively extracting continuous video frame sequences of an original video and a target video, obtaining an original video frame sequence and a target video frame sequence, the target video is a video after frame insertion of the original video; calculating a time domain difference parameter and a structural similarity parameter according to the original video frame sequence and the target video frame sequence; performing optical flow motion estimation according to adjacent video frames of the target video frame sequence to obtain a motion vector of each pixel in the adjacent video frames; performing motion consistency estimation on the adjacent video frames according to the motion vectors to obtain inter-frame correlation parameters; and performing weighted calculation according to the time domain difference parameter, the structural similarity parameter and the inter-frame correlation parameter to obtain a video frame insertion quality result. According to the invention, the accuracy and the automation degree of video frame insertion quality detection are effectively improved.

Embodiments of the present disclosure relate to spherical projection motion estimation / compensation and mode decision. Techniques are disclosed for predictively encoding video data based on a prediction made from a spherical domain projection of an input picture to be encoded and a reference picture as a prediction candidate. A spherical projection of the input picture and the candidate reference picture can be generated. A search can then be performed for a match between a spherical domain representation of a pixel block to be encoded and a spherical domain representation of the reference picture. Upon a match, an offset between the spherical domain representation of the pixel block and the matching portion of the reference picture in the spherical domain representation can be determined. The spherical domain offset can be converted to a motion vector in a source domain representation of the input picture, and the pixel block can be predictively encoded with reference to the matching portion of the reference picture in the source domain representation.

The application provides a video super-resolution method based on a gated mutual attention, comprising the following steps: S1, acquiring a low-resolution video sequence and performing shallow sequence feature extraction; S2, performing joint motion estimation and feature fusion according to the shallow sequence features by using a multi-scale coding and decoding model based on an efficient attention module and a bidirectional parallel distortion module and in combination with optical flow estimation to obtain new sequence features; the efficient attention module comprises a plurality of gated mutual attention modules; S3, adding the new sequence features after Pixelshuffle up-sampling and convolution to the low-resolution video sequence element by element to obtain a high-resolution video sequence. The application provides a video super-resolution method based on a gated mutual attention, and solves the problem that the VSR performance obtained by the existing super-resolution reconstruction technology is not high enough.

The application relates to the technical field of ultrasonic robot intelligent control and medical image processing, and particularly relates to a standard section search planning method and system based on an incremental Gaussian model, which comprises the following steps: collecting five-dimensional position vectors and corresponding image quality data in an ultrasonic robot search process in real time; jointly modeling and training based on the five-dimensional position vectors and the image quality data to obtain a current Gaussian mixture model; calculating the similarity between the current Gaussian mixture model and a historical Gaussian mixture model, and fusing based on the similarity; predicting the image quality of a candidate posture through Gaussian mixture regression, and constructing an improved acquisition function to select a next search target posture in a safe and feasible range; continuously collecting new data and incrementally updating the model to gradually approach a standard section. Through continuous updating and fusion of the incremental Gaussian mixture model, the application can dynamically predict an optimal section position under real-time conditions, and realizes efficient and stable search of the standard section.

The application discloses a kind of multi-frame image fatty liver content detection method and system based on motion inhibition, belong to medical image processing technical field, which comprises: selecting region of interest in patient liver region and setting processing time;Under B mode ultrasonic scanning, liver image is acquired in real time;Liver parenchyma probability weight distribution map is generated to each frame image;Interframe motion estimation is carried out, and the image frame with motion amplitude in preset threshold is screened out;Nonlinear parameter scanning is carried out, and corresponding nonlinear parameter matrix is obtained;Each element in each nonlinear parameter matrix is converted into liver fat content value, and corresponding liver fat content score matrix is obtained;Kalman filtering algorithm is applied to multiple liver fat content score matrixes to carry out fusion filtering processing, and finally liver fat content quantitative value is obtained.The method can realize non-invasive, accurate and quantitative liver fat content detection;It can be integrated into ultrasonic diagnostic equipment and is suitable for hospitals, medical centers and other scenes.

The invention relates to the technical field of satellite weather forecast, and discloses an optical flow-deep learning satellite multichannel data extrapolation method. The method comprises the following steps: acquiring and preprocessing time sequence satellite multi-channel data; estimating a two-dimensional motion field by using a variational optical flow algorithm, and performing preliminary space-time extrapolation in combination with a semi-Lagrange algorithm and mass conservation constraint; constructing a deep learning model integrating space-time convolution and a recurrent neural network, and performing training prediction by combining the preliminary extrapolation result and the original deep features; dynamically fusing the two types of results through an adaptive weight fusion mechanism; and finally, correcting a fusion result based on atmospheric dynamics and radiation transfer physical constraints, and generating a physically consistent multi-channel extrapolation field. According to the method, the advantages of physical motion estimation and data-driven learning are combined, and the precision and rationality of satellite observation extrapolation from minute level to hour level are improved.

The invention discloses a control method and device for a double-galvanometer system and a storage medium, and the method comprises the steps: inputting the real-time motion state information of a first galvanometer and a second galvanometer into a Kalman filter in a laser processing process, and obtaining the predicted motion tracks of the first galvanometer and the second galvanometer at a future moment; judging whether a triggering condition of an avoiding program is met or not according to the predicted motion track; if yes, according to the priorities corresponding to the first galvanometer and the second galvanometer and the motion trail, a smooth transition path corresponding to the avoiding program is generated through an interpolation algorithm, and in the execution process of the avoiding program, galvanometers are controlled to move based on the smooth transition path. Through dynamic prediction and cooperative control, non-stop avoidance in a public area is realized, the processing efficiency and the path smoothness are improved, and the cooperative control precision of the double galvanometers is guaranteed.

A method for estimating a three-dimensional flow of an object between a first two-dimensional image and a second two-dimensional image, the first two-dimensional image including an image of the object in a first configuration, and the second two-dimensional image including an image of the object in a second configuration, the method comprising: inputting the first two-dimensional image, the second two-dimensional image, and a two-dimensional representation of an estimated three-dimensional shape of the object into a convolutional neural network; and using the convolutional neural network to generate the three-dimensional flow of the object between the first two-dimensional image and the second two-dimensional image from the first two-dimensional image, the second two-dimensional image, and the two-dimensional representation of the estimated three-dimensional shape of the object.