102 results about "Motion estimation" patented technology

The invention discloses a visual inertial positioning method based on dynamic target detection and semantic information constraint, and belongs to the field of motion estimation and dynamic environment processing. According to the method, a dynamic target detection mechanism is introduced, the dynamic target is effectively detected based on the target detection network, inertial navigation information and geometric constraints, the dynamic target is effectively recognized in the image processing process, the corresponding dynamic feature points are screened out, the mismatching rate of the dynamic features is remarkably reduced, and high-quality observation input is provided for back-end optimization. In the back-end sliding window optimization stage, a semantic information consistency constraint method is constructed, and the estimation stability of the system in a weak texture area or a repeated texture area is enhanced by utilizing the consistency of feature points in the same semantic area on a geometric structure. Visual inertia pose estimation is realized based on dynamic target detection and semantic information constraint, and high-robustness and high-precision pose estimation can still be realized in a complex environment with dynamic interference of pedestrians, vehicles and the like and severe scene change.

The invention relates to the technical field of advertisement shooting, in particular to a holder self-adaptive tracking control method based on visual identification, which comprises the following steps of: acquiring a scene image digital signal in real time through a camera, then extracting the coordinate, the size and the motion trail of a detection target, and adopting an optical flow motion estimation algorithm in the extraction process, the optical flow motion estimation algorithm can calculate the motion information of the target by analyzing the motion of the pixel points in the image, and is combined with pure extraction of static information such as coordinates and sizes, so that the state of the detected target can be described more comprehensively and dynamically, the location of the target can be known, and the motion of the target can be accurately mastered; and then, according to the motion trail of the detection target, calculating and establishing a mapping relation between a pan-tilt angle and a camera visual field, and calculating the angle of the pan-tilt needing to be adjusted when the detection target shoots the advertisement and the detection target moves through the mapping relation between the pan-tilt angle and the camera visual field, so that the detection target can be always located at the central point coordinate of the image digital signal.

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 discloses a cracking furnace intelligent optimization system control method and system architecture, and belongs to the cracking furnace application field, and the cracking furnace intelligent optimization system control method comprises the following steps: S1, collecting cracking furnace operation time sequence characteristic data and flame combustion video data in real time through a sensor and a high temperature camera; s2, preprocessing the collected multi-modal data, S3, integrating the preprocessed multi-modal data by using federal learning, and constructing a cross-modal causal logic model; s4, designing a deep reinforcement learning framework, introducing the dynamic prediction model of the cracking furnace, and reasoning the recommendation range of each parameter of the optimized operation of the cracking furnace; s5, dynamic process data and the environment-friendly emission prediction model are combined, a data-driven MPC algorithm framework is adopted, and dynamic optimization control over the cracking furnace is achieved; and S6, the operator performs adjustment or selects emergency operation according to the recommended parameters and strategies to realize man-machine cooperative control. Finally, the dynamic optimization control of the cracking furnace can be realized, and the energy consumption is effectively reduced.

The invention relates to the technical field of security and protection monitoring systems, in particular to a full-scene intelligent analysis system and method based on a multi-modal large model, and the system comprises a feature extraction module which is used for obtaining cross-modal data, generating a time sequence feature set according to a cross-modal tensor, and generating a space structure set according to cross-modal image data; the semantic fusion module performs semantic interaction on the time sequence feature set and the space structure set based on a cross-modal structure to obtain a cross-modal data set; the causal generation module generates causal association based on the time sequence feature set and the spatial structure set; generating a causal time sequence association based on the cross-modal data set and the causal association; the dynamic prediction module associates the dynamic learning association mechanism based on the causal timing to generate a dynamic prediction model. According to the invention, the intelligent supervision capability of key places is obviously improved, and the generalization detection capability of novel violation behaviors can be improved, so that complex or unknown risk scenes are covered.

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 mechanical arm intelligent grabbing system and method based on three-dimensional vision. The system comprises a three-dimensional vision system, an image preprocessing module, a model matching module, a grabbing point generation module, a grabbing control module and a dynamic environment self-adaption module. The method comprises the steps of multi-modal reference model collection and feature library establishment, precise hand-eye calibration and grabbing strategy self-learning and multi-modal fusion recognition, and dynamic prediction and self-adaptive grabbing. By adopting the three-dimensional visual recognition system and combining algorithms such as point cloud processing, point cloud method phase generation, point cloud downsampling, rough model matching and fine model matching, the limitation that a traditional mechanical arm system depends on two-dimensional image training and position coordinate point matching is overcome; by generating the grabbing points and calculating the pose coordinates of the grabbing points according to the hand-eye calibration matrix, the problem that the estimated pose of the grabbed object under the camera coordinate system is inaccurate in the prior art is solved. The system is easy to implement and has high practicability and popularization value.

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 computer vision and image processing. The application provides a large field of view underwater camera calibration-free distortion correction method. The disclosure embodiment can adopt a two-stage motion estimation architecture of "region-level progressive TPS plus pixel-level residual flow compensation", decouple different complexity motion representations, and balance global deformation fitting and local detail preservation; a lightweight task classifier is designed to identify distortion types based on TPS control point distribution features; a prompt learning module is introduced to modulate decoder features through learnable prompt words and task probability vectors to achieve single-model multi-task adaptive correction; a multi-task joint training strategy and a hybrid loss function are used to strengthen the generalization ability of the model to different distortion types and support arbitrary resolution image input.

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.

The embodiment of the invention provides a data processing method and device and track capturing equipment, and the method comprises the steps: firstly obtaining magnetic data and video frame data in a motion process, and then obtaining a motion track of a motion object in the motion process according to the magnetic data and the video frame data. That is to say, the motion trail of the moving object is obtained through the magnetic data obtained through the magnetic positioning technology and the video frame data obtained through the video collection device. The method does not need to depend on experience and judgment of observers, and is not easily interfered by external factors. In addition, the method can make full use of the high-frequency sampled magnetic data of the magnetic positioning technology to dynamically predict and compensate the low-frame-rate video frame data, and can improve the positioning precision of the motion trail of the moving object compared with the method of obtaining the motion trail of the moving object by using single video frame data.

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 embodiment of the invention discloses a deblurred view synthesis method and device, electronic equipment and a storage medium, and relates to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: obtaining an initial camera pose through three-dimensional reconstruction software, and employing a cubic Bezier curve to model a camera motion track; dynamically predicting three-plane resolution by using a feedforward neural network according to the track length; self-adaptive three-plane downsampling is carried out based on the predicted resolution, and a clear image is generated through a volume rendering technology; and finally, obtaining a full-resolution three-plane field through a loss optimization and gradient descent algorithm, and generating a clear image corresponding to the pose of the target camera according to the learnable parameters and the three-plane field. According to the method, the problems of low sampling efficiency, long training time and rendering quality degradation under a long track when facing camera motion tracks with different lengths in the prior art are solved, the training efficiency is remarkably improved, the memory occupation is reduced, and the generalization ability and reconstruction quality of the model are enhanced.

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 invention belongs to the technical field of construction progress early warning, and particularly relates to a project construction progress early warning method, system and equipment based on deep learning, and the method comprises the steps: carrying out the image recognition of a construction scene image based on an improved R-FCN algorithm, obtaining image recognition data, enabling the image recognition data and quality monitoring index data to form multi-modal input data, and carrying out the construction progress early warning. Inputting the data into an LSTM deep learning model to predict future quality monitoring index data; and outputting a risk membership degree and a D-S evidence theory for predicted future quality monitoring index data through a cloud model to obtain a fusion result, weighting the risk membership degree and the fusion result to generate a comprehensive risk index, and realizing construction progress grading early warning according to the comprehensive risk index. According to the method, on one hand, the dynamic prediction precision of the quality monitoring index data can be improved through the multi-modal input data, the method can dynamically adapt to complex construction scenes, on the other hand, conversion from quantification to qualitative is achieved through the comprehensive risk index, and finally construction progress grading early warning is achieved.

The video super-resolution system comprises a motion estimation device, a mapping device and a neural network super-resolution device. The mobile estimation device calculates an optical flow according to the current frame and a previous frame received from the memory. The mapping device maps a previous frame and a previous output received from the memory according to the optical flow to generate a mapped frame and a mapped output, respectively. The neural network super-resolution device performs feature extraction on the current frame, the mapping frame, the mapping output and the count value to generate at least one feature, and performs deep learning on the at least one feature and a previous hidden state of the previous output to generate a current hidden state and a deep learning result, and performing feature extraction on the deep learning result to generate a current output. The neural network super-resolution device stores the current frame, the current hidden state, and the current output to a memory.

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 a video intelligent compression and transmission method and system under a narrowband network. An AI video coding algorithm is introduced to a device end, and in combination with intra-frame partitioning, inter-frame motion estimation, edge detection and frequency domain conversion, an asymmetric dense feature array is generated and compressed. The code rate is dynamically adjusted in combination with the network state, so that efficient continuous video transmission is realized; aI super-division enhancement is adopted at a decoding end, and H.265 secondary coding is carried out, so that clear image quality and stable transmission are guaranteed. The system is suitable for remote monitoring, vehicle-mounted, airborne and other low-bandwidth scenes, and has the advantages of high compression ratio, low bandwidth occupation, smooth video, high real-time performance and the like.