152 results about "Motion perception" patented technology

The invention relates to a self-localization and motion perception method based on deep learning, and the method comprises the following steps: multi-modal perception: collecting RGB frames and event streams through employing a DAVIS346 event camera, obtaining texture and depth information through employing an RGB-D camera, and supplementing 3D structure data through a laser radar; hybrid optical flow driven motion perception: adopting a double-branch architecture of an improved RAFT network and an event optical flow private network; multi-modal 3D detection and trajectory management: fusing multi-modal data based on VoxelNeXt-Lite to generate a 3D detection frame, filtering false detection by combining point cloud density clustering and an event density threshold, then constructing a space-time diagram associated trajectory through a Spatio-Temporal Graph Transformer, and optimizing pedestrian trajectory prediction continuity by using an LSTM (Long Short Term Memory) model perceived by a gait cycle; multi-view semantic graph matching: constructing a 3D semantic voxel map containing vertical features, calculating scene similarity in combination with top view NetVLAD features and deformable graph matching, and dynamically adjusting semantic weight by using a situation encoder and knowledge graph reasoning; and carrying out multi-sensor fusion and robust positioning.

The invention discloses a human motion generation method and system based on text alignment, and belongs to the technical field of computer vision and deep learning. According to the method, a two-stage training mode is adopted, in the first stage, a quantized variational auto-encoder model for reconstructing human motion is trained at first, then parameter freezing is carried out on a decoder network in the quantized variational auto-encoder model and a pre-trained text-to-motion cross-modal retrieval model, the decoder network and a bidirectional mask Transform model form a training framework for training, and the quantized variational auto-encoder model and the pre-trained text-to-motion cross-modal retrieval model form a training framework for training; and after training is completed, reasoning tasks are performed. According to the method, by improving a text encoder, adding semantic alignment loss and adopting a bidirectional mask type Transform architecture, the overall motion perception ability is enhanced, semantic alignment of motion is generated, the generated human motion sequence better conforms to original data distribution, the generated human motion sequence and an input motion description text are more semantic-consistent, and the motion perception ability of a human body is improved. And the motion generation quality is improved.

The invention relates to the technical field of physical training intellectualization and man-machine interaction, in particular to an interactive physical education method and system based on augmented reality driving, and the method comprises the following steps: S1, collecting three-dimensional motion track data and muscle activation time sequence data; s2, generating kinematic deviation parameters and physiological deviation parameters; s3, generating an AR correction instruction set containing a joint pose correction vector and a muscle activation intensity adjustment suggestion based on the kinematics deviation parameter and the physiology deviation parameter; s4, projecting the AR correction instruction set to the field of view of the trainee in real time through AR display equipment; and S5, generating a physical education efficiency report containing the action precision improvement index and the neuromuscular control optimization scheme. According to the intelligent physical education system, by fusing multi-source motion perception, deviation analysis and AR visual correction, synchronous optimization of motion precision and neuromuscular control ability is realized, and an intelligent physical education system with real-time interaction characteristics is constructed.

The invention discloses a reference video object segmentation method based on semantic consistency and motion perception. The method comprises the following steps: 1, constructing semantic prompt information and video frame information of a reference video object segmentation data set; 2, preprocessing the reference video object segmentation data set; 3, establishing a reference video object segmentation model based on semantic consistency and motion perception: designing a double-branch decoupling strategy for decoupling feature information at semantic and visual levels so as to extract static and motion information of text description and visual features; a hierarchical motion sensing module is designed to capture and align motion information between different frames, and analyze short-term and long-term motion information, so that the model obtains a sensing ability for a long-term motion mode; the semantic consistency module is designed to align semantic description and video features, so that the accuracy of target selection and the integrity of masks are improved, and false detection of negative samples is avoided; a perceptual dynamic fusion mechanism is designed to be used for embedding text information into a visual feature space, so that visual features can obtain text semantic information, and the cross-modal understanding ability of the model is enhanced; 4, constructing a loss function, updating model parameters, setting training parameters, and training to obtain an optimal weight; and 5, detecting the test set image based on the optimal weight to obtain a final segmentation result. According to the method, static and dynamic information is effectively decoupled, the sensing ability to an object motion mode is enhanced, and the segmentation performance is improved.

The invention provides a digital human body modeling system and method based on space-time consistency Gaussian sputtering, and the system comprises a Gaussian set construction module which is used for constructing an initial Gaussian set based on a collected monocular RGB video sequence of human body motions; the non-rigid deformation module is used for updating a pre-constructed standard Gaussian set by adopting a Gaussian decoder based on the initial Gaussian set to obtain a dynamic Gaussian set containing non-rigid deformation; the space-time constraint module is used for applying space-time consistency constraint to the dynamic Gaussian set to obtain the dynamic Gaussian set of the space-time consistency constraint; the rigid deformation module is used for obtaining an attitude-driven Gaussian set based on the rigid deformation of the linear binding skeleton; and the projection rendering module is used for rendering the attitude-driven Gaussian set into a Gaussian image under the current frame attitude. According to the method, a space-time consistency mechanism of motion perception is combined, and the consistency problem of non-rigid deformation in a monocular video is effectively solved.

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

The invention relates to the technical field of music teaching assistance, and particularly discloses an intelligent music teaching assistance system and method based on action perception, which can utilize a deep learning algorithm based on artificial intelligence and image analysis to automatically analyze playing actions of students. And performing action difference comparison and time sequence analysis with standard music playing actions of the musical instrument to identify problems existing in the actions of the students, and providing targeted feedback and improvement suggestions related to the continuity of the playing actions of the students, thereby assisting intelligent music teaching. The teaching efficiency can be improved, students can be helped to master correct playing skills more quickly, and the learning experience is improved.

The invention relates to the field of artificial intelligence, can be applied to service system platforms such as medical treatment and health, and discloses a motion evaluation method, device and equipment based on multi-modal perception and a medium, and the method comprises the steps: collecting multi-modal sensing data through different types of sensors in the motion process of a user; performing data fusion processing on the multi-modal sensing data to generate fused sensing data; key point identification is carried out according to the multi-modal sensing data, and action posture information of the user is acquired according to a key point identification result; inputting the fused sensing data and the motion posture information into a motion evaluation model, evaluating the motion completion quality of the user, and outputting a motion evaluation result; and according to the motion evaluation result, displaying corresponding motion guidance information in the interactive interface through the digital human image. Through multi-modal action perception and intelligent action evaluation, the action of the user is accurately monitored and evaluated in real time, targeted guidance is provided, the user is helped to accurately master action essentials, and the teaching quality is improved.

The invention discloses a high-efficiency time sequence optical flow method, system and device for fusing event and image information and a medium. The method comprises the following steps: constructing a time sequence feature extraction module; constructing a double-branch collaborative iteration optical flow module of the event and the image; constructing a fusion modeling and iterative optimization optical flow module based on cross attention; training a dual-branch collaborative iteration optical flow module of the event and the image and a fusion modeling and iterative optimization optical flow module based on cross attention on a training set of an MVSEC public data set to obtain weights, and testing on a test set of the MVSEC public data set to obtain an optical flow calculation result; the system, the equipment and the medium are used for implementing the method. The method has the advantages of high time resolution, high dynamic modeling capability, fine multi-modal fusion, high estimation precision and the like, the motion sensing performance in a complex scene is remarkably improved, and powerful technical support is provided for an intelligent visual system.

The invention relates to a computer vision technology, in particular to an event camera target detection method based on Euclidean-hyperbolic motion perception, which comprises the following steps: down-sampling original event data, constructing an event graph by using the down-sampled data, and inputting the event graph into a double-space network for target detection; performing local perception feature extraction on the event graph by using a graph convolutional network based on a B-spline kernel function, and projecting the extracted features to a hyperbolic space; extracting global event dependency features from the local sensing features mapped to the hyperbolic space through a learnable curvature hyperbolic graph convolutional network; and decoding the global event dependency feature and inputting the decoded global event dependency feature into a sensing head, and outputting a target detection result by the sensing head. The method solves the problems of noise suppression, topological mapping and hierarchical expression in event data, has good application prospects and popularization value, and is particularly suitable for real-time target sensing tasks in complex dynamic environments such as automatic driving and robot navigation.

The invention relates to an action perception and reconstruction method and device, equipment and a storage medium. The method comprises the following steps: carrying out space-time synchronization and heterogeneous graph modeling on asynchronous data of an electromagnetic sensor and an inertial measurement unit (IMU) through multi-source sensor data preprocessing and fusion base construction; performing deep feature extraction and hierarchical fusion by using a gated attention time-space diagram neural network (ST-GNN); through a dynamic interference confrontation and feature purification mechanism, the robustness of the system in a complex environment is improved; generating a high-fidelity and physiologically reasonable action sequence by adopting a multi-scale space-time generation network (MSTGN) and combining biomechanical constraints; a physical information neural network (PINN) constraint is embedded, and high-precision six-degree-of-freedom (6DoF) pose estimation is realized through a decoupling collaborative framework. According to the method, the precision, robustness and real-time performance of motion sensing and reconstruction are effectively improved.

The invention discloses a teaching implementation method based on combination of motion perception and AI driving, and relates to the technical field of education and teaching. The implementation of the teaching method comprises the steps of multi-modal data expansion and acquisition, cross-dimensional dynamic evaluation and cognitive diagnosis, emotion adaptive feedback and intervention, cross-scene learning map construction and capability migration, and whole-process data closed loop and systematic evolution. And the multi-modal data expansion acquisition comprises the steps of dynamic data acquisition, physiological data acquisition, environmental behavior data acquisition and data preprocessing. The method has the advantages that a multi-modal data acquisition scheme of a common mobile phone camera and a built-in sensor is adopted; the system replaces the traditional dance teaching which depends on special three-dimensional modeling equipment and sensor gloves for boxing teaching, reduces the hardware cost, can realize full scene coverage of families, classrooms, outdoors and the like without professional site deployment, and solves the core problems of high hardware cost and limited scenes in the prior art.

The invention discloses an indoor high-precision positioning system and method based on a UWB technology, and particularly relates to the technical field of positioning, and the system comprises a region division module which divides an indoor space into N dynamic region units based on the structural layout of a building and the coverage range of UWB anchor nodes; establishing a bidirectional mapping relationship between the region and the UWB tag by taking the key building nodes as boundaries; each area unit is associated with moving track data of the label, a space encoder is introduced to generate an area feature vector, and an original three-dimensional coordinate is obtained to represent the positioning characteristic of the area; according to the method, multi-source data of the UWB signal layer data, the motion sensing layer data and the environment sensing layer data are analyzed and processed, the superposition error of environment reflection and hardware deviation is effectively reduced, and the geometric convergence precision of position calculation can be remarkably improved in a complex space.

The invention provides a system for training consistent motion perception ability. The system comprises a training parameter setting module used for setting parameter information related to training; the training image generation module is used for generating a training image according to the training parameters; the man-machine interaction module is used for training information interaction and feedback between an individual and equipment; the equipment comprises display equipment, information input equipment, sound prompt equipment and monitoring equipment; the training process control module is used for controlling the sequence of different test times in the single training process; and the data recording and analyzing module is used for recording and analyzing training data so as to evaluate the overall training process. Through targeted visual stimulation design, rigorous training process control and synchronous combination of training auxiliary equipment, training of the consistent motion sensing ability is effectively realized, and the consistent motion sensing ability of a biological individual is improved.

The invention provides virtual reality-based dance teaching equipment and a teaching method thereof, and the equipment comprises a VR module which is used for providing multi-view immersive visual experience; the motion capture module is used for capturing the joint angle of the dancer in real time and the limb track feedback module and is used for simulating the ground counter-acting force in dancing through vibration or pressure, enhancing motion perception and providing a touch prompt for motion correction; the virtual scene module is used for creating a dance virtual scene; and the action analysis module analyzes the joint action deviation based on multiple view angles, generates an instant score according to the joint angle deviation and the rhythm synchronization rate, and records historical data to generate a progress curve. According to the method, the problems of resource shortage, feedback lag and other pain points in traditional dance teaching are solved, efficient, personalized and interesting dance learning experience is achieved through multi-modal interaction and a dynamic adjustment mechanism, and the application of the method is expected to promote dance education to develop in the intelligent and universal direction.

The invention relates to the technical field of computer vision and robot positioning, and discloses a dynamic 3DGS-SLAM method and system based on Gaussian pyramid and adaptive densification. The method comprises the following steps: generating a semantic mask through real-time dynamic target detection so as to distinguish a static background from a dynamic object; constructing a 3D Gaussian map model of the scene; rendering images in parallel on a full-resolution view and a down-sampling resolution view by adopting a motion perception Gaussian pyramid rendering method, and fusing rendering results by utilizing a dynamic mask so as to eliminate motion edge artifacts; a self-adaptive densification strategy based on a monotonic attenuation function is adopted, the initialization radius of the Gaussian ellipsoid is dynamically adjusted based on the training progress, and high-density initialization and progressive redundancy pruning are executed to optimize the geometric fidelity; and finally, performing coarse-to-fine camera pose optimization, and outputting a precise pose and a high-fidelity static map. According to the method, the problems of tracking drift and rendering artifacts under dynamic interference are effectively solved, and the positioning precision and the rendering quality are improved.

The invention provides an event camera optical flow estimation method based on motion perception and space-time collaborative modeling. The event camera optical flow estimation method comprises the following steps: preparing a data set; dividing and coding the event data into space-time voxel representation; constructing a motion perception space-time aggregation model, wherein the motion perception space-time aggregation model comprises a bidirectional correlation volume module, a spatial feature enhancement module and a space-time motion collaboration module; and training the model by using the training set, continuously optimizing model parameters through a back propagation algorithm, and generating a corresponding high-resolution dense optical flow graph. According to the method, the problem of spatial information loss caused by event data sparsity can be effectively relieved, and the stable perception capability of a motion track is enhanced by using time consistency, so that the accuracy of optical flow estimation is remarkably improved; the method is suitable for task scenes with high requirements on accurate motion perception and real-time response capability in high-dynamic environments such as automatic driving, robot navigation and intelligent monitoring.

The invention discloses a satellite cloud picture sequence prediction method combining deformable convolution and pyramid pooling, and relates to the field of deep learning. The method comprises the following specific steps: (1) preprocessing satellite cloud picture sequence data and dividing into a training set and a test set; (2) building a satellite cloud picture sequence prediction model of a basic encoder-translator-decoder structure; (3) combining a motion perception loss function and an L2 loss function to supervise the model for training; and (4) predicting a satellite cloud picture sequence image. According to the method, a deformable volume operator based on dynamic sparseness is used in an encoder and a decoder to replace the traditional convolution operation, so that the network can adaptively select proper parameters according to the space structure of a cloud picture, softmax normalization in deformable convolution space aggregation is removed, and the robustness of the network is improved. The memory access is optimized to accelerate the running speed; a pyramid pooling structure is used in the translator, so that the calculation process can be simplified, and spatial-temporal characteristics of different scales in the cloud picture sequence can be captured; meanwhile, a motion perception loss function and a traditional L2 loss function are combined to supervise model training, and motion information between adjacent frames of a satellite cloud picture sequence can be further obtained. The method is not only suitable for all satellite cloud picture sequence images, but also can be applied to sequence image prediction in other complex scenes.

The invention relates to the technical field of intelligent vision and image fusion, and discloses an intelligent camera dual-light image fusion method and a dual-light integrated intelligent camera, and the method comprises the steps: collecting an original infrared image and an original visible light image; preprocessing the original infrared image and the original visible light image to obtain a double-path preprocessed image; adopting an improved optical flow algorithm to calculate an optical flow field for the two-way pre-processed image; generating an infrared band motion sensing feature set and a visible light band motion sensing feature set; generating a preliminary fusion image based on the infrared band motion sensing feature set and the visible light band motion sensing feature set; for the preliminary fusion image, obtaining an evaluation result including definition, motion consistency and detail richness evaluation indexes; dynamically adjusting a cross-modal attention weight and a gating value according to an evaluation result; and obtaining an optimized final fusion feature based on the dynamically adjusted cross-modal attention weight and the gating value. According to the invention, the fusion image has no dislocation when the target moves rapidly.

The invention discloses a dynamic scene reconstruction method, computer equipment and a program product. According to the method, picture information and camera poses of the same scene are obtained, a standard field composed of three-dimensional Gaussian primitives is constructed in a three-dimensional space, a deformation field is constructed, the standard field and the deformation field at different times are subjected to joint rendering in a three-dimensional Gaussian splashing mode, and various parameters are trained and optimized according to the difference between a synthetic image and a real image. Performing motion perception according to the motion condition of the three-dimensional Gaussian primitives, taking the primitives with smaller motion as static primitives, and performing partition modeling on the primitives with larger motion in time to obtain a layered dynamic scene model; and calling the model for rendering according to the given time and the camera pose in the reasoning stage, and outputting a target image. According to the method, the reconstruction precision of the high-dynamic target can be improved while the rendering efficiency is ensured, and the continuity and visual quality of the dynamic scene in the time dimension are improved by introducing the time consistency constraint at the boundary of the time subinterval.

The invention discloses an unmanned aerial vehicle target tracking method based on motion perception and feature enhancement, and relates to the technical field of unmanned aerial vehicle video processing. In order to enhance the response capability of a tracking network to a dynamic target and improve the robustness in a motion sudden change scene, a Transform architecture is adopted to extract global features, and a motion sensing module is introduced to perform modeling on target motion. A smooth deformation field is generated through a sparse offset prediction module to realize motion compensation, and the compensation precision is further improved by using a motion decoupling module. In addition, an adaptive motion attention enhancement module is designed, and the module adaptively adjusts the attention intensity according to the motion information. And finally, predicting a target bounding box by the classification regression network. According to the method, the feature expression is enhanced by effectively utilizing the motion information, and the tracking accuracy is remarkably improved.

The invention provides an occupied grid prediction method and device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring continuously acquired multiple frames of visual images and laser point cloud data; converting the visual image to a point cloud scene to obtain image point cloud data; fusing the image point cloud data and the laser point cloud data to obtain fused voxel features; performing motion perception prediction on the dynamic voxels in the fused voxel features through the occupancy flow to obtain motion perception features corresponding to the dynamic voxels; and performing state prediction based on the motion perception features to obtain an occupation prediction result. Visual images are converted into image point cloud data, and the image point cloud data and laser point cloud data are fused in a 3D space to obtain voxel features, so that information loss caused by multi-mode mutual projection can be avoided, and meanwhile, motion perception is directly performed on fused voxels through an occupation stream, so that complete space information can be effectively captured and utilized, and the accuracy of the motion perception is improved. Therefore, more accurate and comprehensive occupied grid prediction is realized.

The invention relates to a handle positioning method and device for sparse features, and the method comprises the steps: constructing a non-rigid multi-sensor joint state estimation model, and building a non-rigid connection through a camera and an IMU (Inertial Measurement Unit); performing joint optimization on the visual re-projection error and the IMU pre-integration error in the sliding window to obtain initial attitude estimation of the handle; based on the number of visible feature points, a geometric degradation state is analyzed through observability, a self-adaptive marginalization strategy of motion perception is established, and a sliding window is dynamically adjusted; based on a self-adaptive marginalization strategy, a prior quality monitoring and multi-hypothesis maintenance mechanism is implemented, initial attitude estimation is optimized, and an optimal handle attitude is output; equipment is realized based on the method. According to the method, high pose estimation precision can still be maintained in a complex interaction scene, long-term drift caused by inaccurate models is remarkably reduced, the survivability under the severe conditions of shielding, rapid rotation and the like is greatly improved, limited visual information in each frame is efficiently and accurately utilized, and the real-time operation capability of a system is ensured.

The invention provides a ship six-degree-of-freedom motion sensing and predicting method and device and a storage medium, and belongs to the technical field of ship motion sensing. Establishing a first measurement equation based on the rotation quaternion and the measurement value; based on the first system state change equation and the first measurement equation, the rotation angular velocity and the linear acceleration of the ship are obtained; establishing a six-degree-of-freedom state-space equation based on the rotation angular velocity and the linear acceleration within the preset duration; based on the rotation angular velocity and the linear acceleration of the ship, the optimal estimation of the system state is determined by adopting an extended Kalman filtering algorithm and a state-space equation, and the six-degree-of-freedom motion response condition of the ship is determined; and according to the six-degree-of-freedom motion response condition of the ship and the six-degree-of-freedom second system state change equation, predicting the six-degree-of-freedom motion change within the specified time. According to the method, the six-degree-of-freedom motion of the ship can be accurately estimated, and short-term six-degree-of-freedom motion of the ship can be predicted.

The invention relates to the technical field of medical information analysis, in particular to a thoracic surgery postoperative complication assessment method and system based on multi-dimensional analysis, and the method comprises the following steps: S1, obtaining a postoperative multi-dimensional monitoring data set; s2, performing standardized preprocessing on the multi-dimensional monitoring data set to generate standardized monitoring data; s3, classifying and integrating the standardized monitoring data into physiological function data, drainage feature data and motion perception data according to data types; s4, chest dynamic pressure change analysis is carried out, and pressure gradient change parameters are calculated; s5, obtaining an abnormal metabolite ratio index; and S6, generating a complication risk assessment report. According to the method, through standardized integration and weighted analysis of the multi-dimensional postoperative monitoring data, accurate quantitative evaluation and graded output of the thoracic surgery postoperative complication risk are realized, and the reliability of early warning and the timeliness of clinical intervention are improved.

The invention discloses a signal enhancement method and device based on an intelligent metasurface and a storage medium, and the method comprises the steps: determining target user equipment according to the measurement report data of to-be-selected user equipment; and determining a prediction signal enhancement period number according to the motion perception data of the target user equipment. And predicting predicted position information of the target user equipment at the plurality of predicted time points. And based on the predicted position information, allocating an intelligent metasurface unit to each target user equipment, and determining a beam output parameter. And signal enhancement is performed on the target user equipment through the intelligent metasurface unit configured with the beam output parameters. According to the technical scheme, the user equipment needing signal enhancement can be accurately judged, and flexible enhancement is achieved according to different conditions of each user. The signal quality and the communication experience are effectively improved, the motion trail of the user is predicted in real time, the accuracy and the practicability of signal enhancement are improved, signal enhancement coverage can be kept in the face of continuous motion, and a powerful guarantee is provided for safe and stable signal connection.

The invention relates to the technical field of motion perception, in particular to an intelligent physical education real-time feedback system and method based on motion perception. The device comprises a joint capturing unit, a deviation coordination unit and an adjustment feedback unit. An angle judgment module calculates an upper arm vector and a front arm vector of an arm part, the dot product of the upper arm vector and the front arm vector is calculated according to the upper arm vector included component of the arm part and the front arm vector included component of the arm part, and the component of the upper arm vector of the arm part is used for calculating the modulus of the upper arm vector; calculating a model of a forearm vector by using a component of the forearm vector of the arm part, calculating an action angle of a joint by combining an upper arm vector and a forearm vector dot product with the model of the upper arm vector and the model of the forearm vector, judging whether the captured whole-body action of the student is a common activity or a movement, and calculating the whole-body action of the student through vector calculation and angle derivation. The action amplitude and speed of the joints can be accurately quantified, the error of subjective judgment is avoided, and the accuracy of analyzing the actions of students is improved.

The invention discloses a reference multi-target tracking method based on language decoupling and fine-grained multi-modal feature alignment, and relates to a computer vision technology. The method comprises the following steps: A, giving a training data set containing a video sequence and language description; b, inputting the video sequence in the step A into a backbone network to extract visual features, and inputting language description into a language model to extract text features; and C, performing multi-modal alignment and fusion through a cross attention mechanism according to the visual features and the language features extracted in the step B. And D, decoupling the language features extracted in the step B into local description and a motion state. And E, inputting the refined features extracted in the step C and the local description extracted in the step D into a static semantic enhancement module to extract target information. And F, associating the current frame target obtained in the step E with the existing trajectory by using a Hungary matching algorithm. G, inputting the matched target features in the step F and the motion state in the step D into a motion perception alignment module to enhance the target recognition capability; the tracking performance of the method is improved.

The present invention relates to the field of target recognition technology, and provides a moving target tracking method, device, electronic device, and storage medium. The method comprises: inputting a historical target bounding box sequence of a tracked target into a motion position representation module of a target tracking model to obtain a historical position embedding feature sequence of the tracked target; inputting a historical position embedding feature sequence of the tracked target into a target position prediction module of the target tracking model to obtain a current position embedding feature of the tracked target; inputting a regional image of a current search area into a visual recognition module of the target tracking model to obtain a current visual feature of the tracked target identified by the visual recognition module based on a target template; and inputting the current position embedding feature and current visual feature of the tracked target into a motion perception fusion module of the target tracking model to obtain a current target bounding box of the tracked target. The present invention improves the accuracy of tracking complex moving targets.

The invention relates to the technical field of computer vision, in particular to a motion perception rockfall intelligent monitoring method and device based on deep learning. The method comprises the following steps: receiving a monitoring video from a monitoring area; constructing a rockfall detection and tracking framework based on a YOLOv8 target detection network and a ByteTrack target tracking algorithm; preprocessing the monitoring image, inputting the monitoring image into a YOLOv8 detection network to carry out moving target feature extraction, and outputting a rockfall detection result; and track matching is carried out by using a ByteTrack algorithm in combination with Euclidean distance and motion direction constraint, and a rockfall motion track is generated. According to the method, a traditional image algorithm and a deep learning algorithm are combined, moving target detection and multi-target tracking are constructed, and rockfall in a complex rock background is accurately captured from multiple dimensions; according to the method, the space coordinate position of the rockfall target can be accurately obtained, a complete movement track can be generated for each rockfall target, statistical information of rockfall events is effectively obtained, and a reliable basis is provided for disaster grade evaluation and early warning.