116 results about "Motion modeling" patented technology

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

The invention discloses a four-dimensional Gaussian video method and a four-dimensional Gaussian video device combined with motion layering, which realize vivid volume video generation and streaming transmission in a dynamic scene. According to the method, a streaming Gaussian Group (GOG) representation is constructed, the representation reconstructs a dynamic scene by using Gaussian point clouds grouped according to a time sequence, and a two-dimensional segmentation result is promoted to a three-dimensional motion layering in each GOG in combination with a motion layering technology to obtain layering of static points and dynamic points, so that the dynamic scene is reconstructed. And modeling a dynamic portion in the scene by predicting a motion offset of the dynamic point. In addition, the invention provides a self-adaptive static point conversion mechanism for converting static points into dynamic points in motion modeling, so that appearance changes (such as shadow movement and reflection changes) of static objects are effectively processed.

The invention provides a zebra fish multi-target tracking method and system based on a space science experiment video. The method comprises the following steps: firstly, obtaining a zebra fish space science experiment video; then, motion information is extracted based on the zebra fish space science experiment video, and the motion information is used for representing a motion state represented by pixel points in the zebra fish space science experiment video; then, multi-modal feature fusion is carried out based on the motion information and the appearance information, and multi-modal fusion features are determined; and finally, target detection and tracking are carried out according to the multi-modal fusion features, and target movement track information corresponding to the target zebra fishes is determined. According to the method, motion feature information representing dynamic changes is extracted through motion modeling based on a self-adaptive threshold value, then the interaction and fusion relation between appearance and motion features is modeled step by step through a heterogeneous graph, and the robustness of target detection, data association and trajectory prediction is enhanced. Therefore, the stability and the accuracy of multi-target tracking of the zebra fish can be effectively improved.

The invention discloses an Ansys-based intestinal tract wall and wall surface motion modeling method and device. The method comprises the following steps: S1, establishing a human body real intestinal tract three-dimensional geometric model; s2, preprocessing the three-dimensional geometric model of the intestinal tract; s3, intestinal wall material parameters are set, and an intestinal wall is simulated; s4, separating a wall surface solid domain from the modified intestinal tract three-dimensional geometric model, carrying out wall surface simulation setting, and dividing grids; s5, by setting load and boundary conditions, wall surface movement setting is achieved in the form of simulating intestinal tract movement; wherein the motion forms comprise creeping, sectional contraction and swinging contraction; and S6, setting solver parameters, and simulating intestinal tract wall and wall surface movement. Loads and constraint conditions can be flexibly adjusted, the variability and adaptability of the model are remarkably enhanced, and the cost of sacrificing the real reflection capability of the model to the actual physiological state is not taken.

The invention discloses a reference video object segmentation method and system based on motion modeling and multi-modal interaction, and the method comprises the steps: taking a video sequence and natural language description as input, and generating a preliminary segmentation mask through a text coding and mask decoder; a Kalman filtering motion modeling module is introduced to predict the motion trail of the target object, and time sequence consistency optimization is carried out on the preliminary segmentation mask; fusing the historical track of the object and the action semantics in the semantic features on the basis of a key action semantic coding module to realize action semantic alignment and mask dynamic correction; the segmentation quality of the current frame is subjected to multi-dimensional scoring based on a representative frame screening mechanism, the representative frame is screened out to update a memory bank, and the long-term tracking stability is improved. According to the method, the problems of target drift, insufficient semantic alignment and memory pollution in a complex dynamic scene in the prior art are effectively solved, and the segmentation precision, robustness and semantic consistency are remarkably improved while the light weight of the model is kept.

The invention discloses a scene three-dimensional reconstruction and vector information extraction method and device based on vehicle return data, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of a time sequence image returned by a vehicle and corresponding pose data, and obtaining a semantic mask and a camera pose of each frame of image in the time sequence image; based on the semantic mask and the camera pose, performing three-dimensional geometric reconstruction on the static scene area in the time sequence image to obtain a static three-dimensional scene model; a dynamic target is separated from the time sequence image according to the semantic mask, three-dimensional motion modeling and independent three-dimensional reconstruction are carried out on the dynamic target, and a dynamic target three-dimensional model is generated; and generating a dynamic three-dimensional scene model and vector labeling information based on the static three-dimensional scene model and the dynamic target three-dimensional model. According to the method, the limitation of automatic driving shadow mode data is overcome, the three-dimensional reconstruction of the scene and the automatic generation of the true value information are realized, the data acquisition cost is further reduced, and the efficiency of automatic driving research and development is improved.

The invention relates to a digital prototype construction system for three-dimensional weaving preform equipment, the system comprises five different model construction methods and four associated construction methods among models, and the five different models are constructed by physical modeling, data modeling, motion modeling, process modeling and simulation. The four associations among the models are the association of a physical model and data, the association of a motion model, a physical three-dimensional model and a data model, the association of a process model, the motion model and the data model, and the association of a simulation result and the data model. According to the method, a virtual production process twin model is constructed, bidirectional real-time interaction with a physical production process is performed, and dynamic updating of a simulation model is driven by using multi-source data, so that the construction process of a digital prototype is standardized, real-time monitoring of the production process is realized, the utilization rate of data is improved, and closed-loop feedback and process optimization are realized.

The invention relates to an underwater single target tracking method based on a wavelet token and a space-time Transform. The method comprises the following steps: firstly, constructing a reference frame sequence, a search frame and a previous frame historical token into a space-time input sequence, and extracting cross-frame features through a Transform encoder; then, Haar wavelet decomposition is carried out on the historical token, and a low-frequency component representing a target structure and a high-frequency component capturing motion details are separated out; then, adaptively fusing the global features and the historical components of the current search frame by using a gating mechanism, and generating a wavelet token; and finally, inputting the wavelet token and the global feature into a prediction head, and outputting a target classification confidence map and a bounding box regression map to determine the position and the scale of the target. According to the technical scheme of the invention, the interference of underwater low-illumination noise can be effectively suppressed through the wavelet token, and the space-time continuity of target motion modeling is maintained in combination with a gating strategy, so that the tracking robustness of an underwater complex scene is effectively improved.

The invention discloses a field personnel behavior identification method based on human skeleton key points. The method is suitable for behavior monitoring of operators in complex production environments such as hydraulic power plants. The method comprises the following steps: performing feature extraction and modeling on skeleton data acquired by camera equipment, and performing multi-scale space-time modeling by adopting a time sequence guide space topology modeling module and a space-time motion modeling network; according to the method, the capturing capability of the dynamic relation between human skeleton nodes is enhanced, the behavior recognition precision of long time span and complex postures is improved, and the robustness and generalization capability of the model in a complex environment are improved; on the whole, the risk behavior of the operating personnel can be effectively identified, the production safety is guaranteed, and the risk caused by human errors is reduced.

The invention discloses a super-realistic digital human detection method based on motion modeling, and the method comprises the steps: carrying out the motion sequence extraction of a designated person based on a human body three-dimensional model; extracting motion features of the specified character based on the motion sequence and a visual model; and for the extracted motion features, mapping the motion features into a feature space, constructing an action feature distribution hybrid model, calculating an abnormal score of the digital human under normal feature distribution based on the model, and completing digital human counterfeiting detection according to the abnormal score. According to the method and the system, a super-realistic digital human forgery detection method based on action modeling is matched with an existing action capture technology, more universal action characteristics can be learned, so that the effect of detecting the super-realistic digital human is achieved, and the method and the system have relatively high generalization.

The invention relates to the technical field of human body breathing micro-motion modeling and holographic imaging, and particularly discloses a radar human body breathing micro-motion 4D modeling and high-resolution holographic imaging method, which is characterized by comprising the following steps of: performing irradiation perception on a human chest target by adopting a multiple-input multiple-output plane radar array; a thoracic cavity target is regarded as a'vector signal source ', namely the thoracic cavity target is equivalent to a set of scattering points of which the spatial positions are mutually discrete; in the geometric model, a graphical representation 3D space coordinate system is established by taking the center of a radar array as an original point and a plane where the array is located as an XOZ plane. According to the radar human body breathing micro-motion 4D modeling and high-resolution holographic imaging method, the micro-Doppler principle is combined, and the micro-Doppler information of each scattering point in the fourth dimension, namely the slow time dimension, is obtained based on the phase decomposition and extraction technology, so that 4D precise reduction of the fluctuation process of the thoracic cavity of the human body is realized.

The invention relates to the technical field of computer vision, in particular to a multi-target tracking method combining a time convolution network and a self-attention mechanism, which comprises a motion model based on a TCN, dynamic ReID feature updating based on track confidence, and a rematching strategy used for correcting wrong association caused by target interaction shielding. A TCN network is combined with an attention mechanism to be used for motion modeling, and nonlinear motion prediction of a track is achieved. In addition, a track confidence coefficient is provided to measure the robustness of the track, and the ReID features are dynamically updated based on the track confidence coefficient. Meanwhile, in order to make up for wrong association caused by target interaction shielding, a rematching module is designed, and the effectiveness of the rematching module is proved through experiments. In addition, each module of the system has a modular characteristic and a relatively strong generalization ability, so that the system is suitable for a wider real scene, and future research work is possibly stimulated.

The invention discloses a multi-sensor cooperative robot target positioning system, and relates to the technical field of robot positioning, in particular to the multi-sensor cooperative robot target positioning system. The objective of the invention is to solve the problems of reduced positioning precision and insufficient robustness caused by sensor data failure, insufficient cooperation mechanism and motion interference in an extreme environment. The system comprises a multi-source sensing module, a collaborative fusion module, a motion modeling module and a closed-loop control module, a robot pose adjustment instruction is generated through multi-sensor data acquisition, space-time alignment and confidence weighted fusion, dynamic motion modeling and error compensation, and closed-loop collaboration of positioning and motion is achieved. According to the invention, the positioning precision, robustness and continuous operation capability of the system in an extreme environment are effectively improved.

The present invention belongs to the fields of machine learning and visual object tracking, and provides a visual single-object tracking method assisted by motion information. The present invention models the camera motion and the target motion during the tracking process respectively. For the camera motion modeling method, the present invention uses a feature point matching algorithm to calculate the transformation matrix between adjacent frames, and gives the target offset caused by the camera motion. For the target motion modeling method, the present invention uses a convolutional long short-term memory network to estimate the future speed and position of the target through the target historical motion information. After introducing motion information for assisted tracking, the present invention can significantly improve the ability of the tracking algorithm to cope with challenges such as illumination changes and occlusions, enhance the robustness of the tracking algorithm, and has a low computational amount, capable of meeting the real-time tracking requirements.

The invention relates to the technical field of ocean engineering operation motion modeling and control, and particularly discloses a method and a system for calculating influence of dynamic response of a separation supporting device and a buffer coupling device on a ship pose in a floating support installation process. The method comprises the following steps: acquiring initial position information of a separation support device and a buffer coupling device at a previous moment; acquiring vertical position and attitude information of the dynamic positioning ship and the upper module; calculating the supporting force of the separation supporting device and the buffer coupling device at the current moment; calculating the vertical position and attitude information of the upper module and the dynamic positioning ship at the current moment based on the supporting force and the external ballast water load; outputting relevant parameters and judging whether the installation is completed or not. According to the method, a multi-degree-of-freedom coupling dynamic model is constructed, high-precision real-time simulation of device dynamic response and ship motion attitude in the continuous unsteady-state load transfer process is realized, key parameter monitoring and risk early warning are provided for floating mounting operation, and the operation safety and efficiency are improved.

The invention relates to a multi-target tracking method under monitoring of an unmanned aerial vehicle, and belongs to the technical field of intelligent monitoring. According to the method, a traditional DeepSORT algorithm is optimized from three aspects of feature extraction, a matching strategy and motion modeling aiming at the problems of small target, frequent shielding, nonlinear motion and the like in an unmanned aerial vehicle scene. The method comprises the following steps: firstly, replacing an original feature extraction module with a lightweight OfficientNet network, and improving the small target feature discrimination through a composite scaling strategy and an MBConv module; secondly, providing a dynamic matching strategy based on EIoU, fusing position, shape and motion information, and reducing the mismatching rate of dense scenes; and finally, introducing extended Kalman filtering to process nonlinear motion trail prediction. According to the method, the MOTA index on a VisDrone data set reaches 85.6% and is improved by 12.3% compared with an original algorithm, and the tracking robustness in a complex scene is remarkably improved while the real-time performance is kept.

The invention discloses a three-dimensional target tracking method, system and device based on space-time enhancement and a medium, and relates to the technical field of automatic driving. The method comprises the following steps: inputting three-dimensional point cloud sequence data into a trained SMTrack network for processing to obtain a final bounding box prediction result. Wherein the SMTrack network comprises a target specific encoder, an STFM module and an STT module which are connected in sequence; wherein the target specific encoder is used for extracting target specific features from a point cloud sequence; the STFM module is used for modeling appearance information and motion information in stages according to the specific features of the target, and generating a preliminary bounding box prediction result; the STT module is used for optimizing the preliminary bounding box prediction result; according to the method, a twin structure is adopted, features are extracted from a historical frame sequence and a current frame sequence, motion modeling of part sensing and a coarse-to-fine bounding box regression mechanism are introduced, and the target positioning precision can be greatly improved.

The present disclosure provides systems, apparatuses and methods that provide probe-cavity location and motion data based on probe location and respiration data.

The invention discloses a point cloud gait recognition method based on SE (3) motion modeling and a computer program product, and the method comprises the steps: obtaining the motion gait features of point cloud data according to the space-time neighborhood and the spatial neighborhood of the point cloud data, and the motion gait features are the SE (3) features of each point in the point cloud data; obtaining a preset interface set according to the point cloud data and the motion gait features, obtaining a plurality of optimal interfaces from the preset interface set, adjusting a space-time neighborhood and a spatial neighborhood according to the plurality of optimal interfaces, and obtaining adaptive hierarchical motion gait features; and projecting the adaptive hierarchical motion gait features to a camera plane to obtain preset feature representation, learning the preset feature representation by using a multi-scale feature fusion network, and outputting a gait recognition result. According to the method, the problems of shielding, cross-view and dynamic motion characterization in a complex scene can be solved, and the biomechanical consistency and scene adaptability of gait features can be remarkably improved.

The invention provides an intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller. The intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller comprises a guidance signal module, a speed optimization module, a parallel path tracking controller module, a data-driven learning predictor module and an actual controller module. The data-driven learning predictor module receives position information, course angle information and speed information from the actual intelligent ship system module, and the data-driven learning predictor module receives parallel path tracking control force and torque from the parallel path tracking controller module; the data driving learning predictor module sends estimation information of the position and course angle of a virtual intelligent ship and estimation information of the speed of the virtual intelligent ship to the guidance signal module and the parallel path tracking controller module. According to the method, the self-adaptive capacity of the ship under the complex sea condition is enhanced, the collision risk is reduced fundamentally, and an effective solution is provided for the navigation scene with the high safety requirement.

Computer implemented biometric methods and systems incorporate sensing biophysical phenomena, translating the phenomena into digital data and transmitting the data to a series of servers operating in an open feedback loop to generate a module. A biometric networking system can include a biometric monitoring cloud computing platform with AI / machine learning augmented models are generated to make user assessments, programs and confidence scores to the healthcare provider systems. The AI / machine learning models can be used by the biometric monitoring network to generate health-related AI processes that analyze relationships treatment techniques and outcomes. AI techniques can be used to calculate movement modeling and confidence scoring including support vector machines, neural networks, and decision trees. The biophysical phenomena may include biometric parameters based on data, such as medical history, exertion, sleep, temperature, cardiovascular events, respiratory events, and muscle and blood pH.

The invention discloses an environment-based multi-agent motion modeling method and device. The method comprises the following steps: acquiring task demand information; constructing a multi-agent dynamic environment management model; and processing the task demand information based on the multi-agent dynamic environment management model to obtain simulation result information. According to the method, the multi-agent dynamic environment management model and the multi-agent self-adaptive motion resolving model are utilized, high-precision prediction and simulation of the formation motion state of the multi-agents under different environment conditions are achieved, and the authenticity and application value of analog simulation are improved.

The application discloses a reference video object segmentation method and system based on motion modeling and multi-modal interaction, which comprises the following steps: taking a video sequence and a natural language description as input, generating a preliminary segmentation mask through a text encoding and mask decoding module; introducing a Kalman filter motion modeling module to predict the motion trajectory of the target object and optimize the time sequence consistency of the preliminary segmentation mask; based on a key action semantic coding module, fusing the action semantics in the historical trajectory and semantic features of the object, realizing action semantic alignment and dynamic mask correction; based on a representative frame screening mechanism, multi-dimensional scoring is performed on the segmentation quality of the current frame, and representative frames are screened out to update the memory bank, thereby improving the long-term tracking stability. The application effectively solves the problems of target drift, insufficient semantic alignment and memory pollution in the prior art in a complex dynamic scene, while keeping the model lightweight, significantly improves the segmentation accuracy, robustness and semantic consistency.

According to the multi-sensor fusion evaluation algorithm and device based on the continuous time sequence, modeling is carried out based on continuous time, physical reality is better met, motion modeling with better precision is achieved, intra-frame motion can be accurately described, smoother and more accurate track estimation can be provided, and the method and the device are suitable for being applied to multi-sensor fusion evaluation. Especially, the advantages are obvious in high-speed and high-frequency vibration scenes; meanwhile, according to the technical scheme disclosed by the invention, when LiDAR / Camera matching fails, the constraints of the IMU and the GNSS are seamlessly and continuously applied to the whole track through a continuous time model instead of only acting on a discrete frame, so that error accumulation is greatly inhibited, system collapse is avoided, the robustness of a degraded scene is enhanced, application in specific fields such as surveying and mapping is greatly facilitated, and the method is suitable for popularization and application. And the method has the capability of flexibly adding constraints.

The invention provides a deep learning AUV adaptive navigation control device under complex sea conditions, and relates to the field of AUV navigation devices. The deep learning AUV adaptive navigation control device under the complex sea condition comprises environment perception, environment modeling, motion modeling, disturbance modeling, a central processing module, implementation control, result summarization, data storage, training model creation and manual control, and the environment perception is divided into a water flow sensor, a wave sensor, an ultrasonic sensor, a gyroscope and a GPS module. According to the system, the real-time learning capability is set, the system can update and optimize the control model while executing the task through the online learning technology, and the system can immediately utilize new environment data for training to adapt to constantly changing conditions, so that the accuracy and efficiency of decision making are improved, and the system is suitable for large-scale popularization and application. The continuously improved capability enables the AUV to more flexibly execute tasks in complex and dynamic sea conditions, and improves the success rate and efficiency of the overall operation.

The invention discloses a human body surface dynamic reconstruction method based on a monocular video, and the method comprises the steps: extracting a key frame of human body motion from the monocular video, and obtaining an RGB image of the key frame, a mask, parameters of an SMPL human body template, and internal and external parameters of a camera; extracting a real normal vector diagram corresponding to the key frame, and constructing a basic data set; the basic data set is used for training a multi-stage progressive human body reconstruction framework, the framework comprises human body overall motion modeling serving as a first stage, local surface dynamic deformation modeling serving as a second stage and surface appearance and illumination modeling serving as a third stage, and an optimal reconstruction model is obtained through training; and extracting a new view angle frame of human body motion from the monocular video, obtaining an RGB image of the new view angle frame, parameters of the SMPL human body template and internal and external parameters of the camera, and inputting the RGB image, the parameters of the SMPL human body template and the internal and external parameters of the camera into the optimal reconstruction model to generate a human body dynamic reconstruction result under the conditions of a new view angle, a new posture and new illumination, thereby realizing high-quality rendering output with geometric details and appearance consistency.

The present disclosure provides a monocular image-based unsupervised three-dimensional scene online reconstruction method and device. The method of the present disclosure comprises: obtaining a static background mask and a mask of each dynamic object instance of a current frame monocular image through semantic segmentation, obtaining a surround view synthesis image through a static multi-view generator, determining the motion parameters of each dynamic object instance through motion modeling, obtaining a multi-view depth map based on the surround view synthesis image through a depth estimation network obtained through self-supervised training, obtaining a local depth map of each dynamic object instance through a local depth estimation network obtained through self-supervised training, and obtaining a complete 3D scene representation through the multi-view depth map, the local depth map of each dynamic object instance, and the motion parameters thereof. The present disclosure can avoid true value dependence, effectively reduce hardware cost, and at the same time improve the reliability and robustness of monocular image three-dimensional scene reconstruction.

The present application belongs to the field of elastic motion modeling of air defense missiles, and particularly relates to an elastic motion modeling method for air defense missiles with trajectory control and direct-gas combined control, aiming at solving the problem that the traditional modeling method is insufficient in covering trajectory control force, thus being difficult to realize accurate modeling of the elastic motion of air defense missiles with trajectory control and direct-gas combined control. The present application comprises: calculating the kinetic energy T of the missile system caused by the bending deformation of the elastic missile body, the elastic potential energy U of the missile system, and the damping energy D of the missile system caused by the bending deformation of the elastic missile body; calculating the generalized force Q acting on the missile body corresponding to the virtual work through the virtual work principle i ; based on the kinetic energy T of the missile system, the elastic potential energy U of the missile system, the damping energy D of the missile system, and the generalized force Q i , constructing the dynamic equation of the elastic missile body through the Lagrange equation. The present application corrects and improves the problem of insufficient coverage of trajectory control force in the traditional modeling method, and has high accuracy and precision in the elastic motion modeling of air defense missiles with trajectory control and direct-gas combined control.

This invention belongs to the field of aerodynamics technology for rotating projectiles, specifically relating to a numerical simulation method for the multi-axis angular motion of a rotating projectile. Based on the projectile's external shape model, a spherical flow field mesh is generated, dividing the mesh into an inner and outer domain. Data is transferred via interpolation at the interface. The global computational domain is used to calculate the steady-state flow field. After flow field convergence, the steady-state flow field parameters are used as the initial flow field for multi-axis angular motion simulation. Multi-axis angular motion modeling is performed on the inner domain mesh, obtaining the coordinates of the inner domain mesh nodes at different times. Multi-axis angular motion simulation is then achieved, including unsteady flow field calculations. The flow field simulation is realized through sliding mesh technology and mesh dynamic motion technology. This invention solves the difficult problems of multi-axis angular motion modeling, complex motion mesh implementation, and complex flow simulation, and streamlines the multi-axis angular motion simulation process, providing a reference for engineering applications.

The application provides a coding method and device in AIGC generation. The decoding method in AIGC generation of the application comprises the following steps: obtaining first feature information and motion information of a current frame based on generated feature information output by an AIGC generation model, wherein the motion information is used to represent inter-frame correlation; performing motion modeling according to the first feature information and the motion information of the current frame to obtain second feature information of the current frame, wherein the motion modeling is used to compensate the first feature information of the current frame based on the motion information; and obtaining reconstructed content of the current frame according to the second feature information of the current frame. The application can improve the time domain compression efficiency and the quality of the reconstructed content in AIGC.