19 results about "Pose estimation algorithm" patented technology

The invention provides a portrait animation generation method and system based on a reversible encoder and dynamic optimization, and relates to the technical field of video portrait reconstruction, high-precision portrait animation generation is realized through multi-module collaborative innovation, multi-scale appearance features are extracted by using a VAE encoder, identity vectors extracted by ArcFace and global appearance embedding of CLIP are combined, and the portrait animation generation method and system based on the reversible encoder and dynamic optimization are realized. Constructing a multi-modal characteristic system; a high-precision attitude estimation algorithm is adopted to extract fine joint points, and an error between a generated attitude and an original attitude is calibrated in real time through a dynamic optimization strategy, so that the attitude driving accuracy is remarkably improved. A reversible residual block is designed to realize lossless two-way transmission of features, and the problem of information compression of a traditional encoder is avoided; a high-frequency detail injection mechanism is introduced, high-frequency features such as facial textures are extracted through a pre-training model and fused with shallow features, an attention mechanism guided by identity features is introduced based on the generation process of a diffusion model, and the consistency of character identities in the generation process is ensured.

The invention discloses an intelligent recognition method and system for grabbing scattered parts, and relates to the technical field of disordered grabbing. In order to solve the problems of high data labeling cost, low occlusion scene pose precision, difficulty in cross-domain generalization and the like in the prior art, a full-process closed loop of'simulation data synthesis-multi-modal segmentation-pose estimation-semi-supervised training 'is constructed: a field adaptive simulation data set is generated through PyBullet + PyRender, and a MaskNet algorithm is obtained based on ViT improved YOLO11 to realize multi-modal instance segmentation; a MaskPosenet two-stage pose estimation algorithm is designed, and cross-domain generalization is realized in combination with pseudo-label semi-supervised learning of a cosine scheduling threshold. According to the method, the data cost is greatly reduced, the complex scene capturing precision and generalization ability are improved, and the method is adaptive to industrial scenes such as logistics sorting and flexible manufacturing and has extremely high application value.

The invention relates to the field of computer vision and intelligent manufacturing, in particular to a vehicle charging port image marking method and device, and the method comprises the steps: obtaining a vehicle charging port image, and carrying out the local frame selection processing; sequentially carrying out graying processing, contrast-limited adaptive histogram equalization processing and median filtering processing; carrying out edge detection by using a Canny edge detection algorithm; adopting a Hough circle detection algorithm to generate an initial position parameter of the circular hole; or, adopting a pre-trained convolutional neural network semantic segmentation algorithm to carry out pixel-level prediction, and extracting corresponding edge points as initial position parameters of the circular holes; if it is judged that the precision of the initial position parameter does not meet the preset labeling requirement, click operation is executed, and a correction point set is formed; and based on the correction point set and the edge points corresponding to the initial position parameters, fitting an ellipse to obtain a marking result of the charging port circular hole. The method can improve the labeling efficiency, guarantees the labeling precision and stability, and is adaptive to subsequent deep learning segmentation and pose estimation algorithms.

This invention discloses a fault-tolerant method for quay crane pose estimation based on multi-sensor fusion, belonging to the technical field of port automation equipment. When a quay crane loses connection due to a network failure, the system automatically detects the pose loss by comparing and analyzing pre-registered quay crane IDs and placement order information with real-time received data. Upon detecting a missing quay crane position, the system immediately reports an error and records the disconnection information. Different intelligent inference strategies are employed based on the quay crane's position order within the area: for quay cranes in the middle position, the median value of the adjacent quay cranes is used as the initial pose; for quay cranes at the edge position, the approximate pose is inferred based on the positions of adjacent quay cranes and the overall trend. This invention employs a point cloud matching-based pose estimation algorithm, achieving high-precision relative pose estimation through multi-sensor fusion of LiDAR and visual cameras, significantly improving the fault tolerance and operational continuity of the port automation system.

The invention provides a laser radar position identification method based on point cloud structure features. Comprising the following steps: S1, acquiring three-dimensional point cloud data acquired by a laser radar, and generating a bird's-eye view BEV image reflecting point cloud spatial distribution characteristics; s2, extracting two-dimensional key points from the bird's-eye view BEV image, and dividing the corresponding three-dimensional key points into point features, line features and surface features; s3, constructing a global hash descriptor reflecting the overall structural characteristics of the point cloud and a local probability descriptor reflecting the local structural characteristics; s4, performing rapid candidate retrieval based on the global hash descriptor to obtain a candidate frame set for position identification; and S5, carrying out relative pose solving on the candidate frame and the current frame by adopting a robust pose estimation algorithm, and determining a final position identification result. According to the invention, through BEV image conversion, double descriptor cooperation and multi-dimensional innovation of a two-stage strategy, core bottlenecks of efficiency and robustness imbalance and incomplete feature representation in laser radar position identification are systematically solved.

The invention provides an object attitude estimation method and system based on three-dimensional feature mapping and visual angle aggregation, and belongs to the technical field of attitude estimation, and the method is applied to a system comprising an image processing module, a three-dimensional feature mapping module, a visual angle aggregation module and an attitude estimation module. The method specifically comprises the steps of performing feature extraction on a template image and a target image, performing three-dimensional space mapping on template image features, determining template three-dimensional features and view angle information, determining the view angle information of each target image feature and the corresponding template three-dimensional feature through feature matching, and determining the target image feature according to the view angle information. And obtaining a matching relation between target image features and template three-dimensional features through view angle aggregation, selecting an attitude estimation algorithm to solve an alternative rough attitude estimation result, and performing iterative optimization by taking the alternative rough attitude estimation result as an initial value of an attitude optimization network to obtain an object attitude estimation result. According to the method, the error transmission risk in attitude estimation can be effectively reduced, the accuracy is guaranteed, the calculation complexity can be reduced, and the estimation efficiency is improved.

The invention provides a mechanical arm control method for tracking a dynamic target in real time, and belongs to the technical field of mechanical arm control, and the mechanical arm control method comprises the steps that a vision measurement system is constructed through a binocular infrared camera and a visible light camera, and a marker image on the dynamic target is captured; based on a visual detection algorithm, two-dimensional code data corresponding to the marker image are extracted for tracking identification detection, and when it is detected that the dynamic target is a to-be-tracked target, real-time position information and real-time attitude information of the to-be-tracked target are calculated through a perspective attitude estimation algorithm; performing fusion processing on low-frequency vision measurement data constructed by the real-time position information and the real-time attitude information and the high-frequency motion signal based on an inter-frame difference method to obtain real-time pose data; and the visual servo controller controls the mechanical arm to move according to a motion optimal solution obtained based on the mechanical arm kinematics model. According to the method, real-time tracking of the dynamic target is achieved, and the method has high operation grabbing efficiency and is suitable for various task scenes.

This invention discloses a machine vision-based method, system, device, and medium for monitoring learning attention, specifically relating to the field of object pose determination in image analysis. It addresses the perspective distortion problem caused by changes in the distance between the learner's face and the camera in existing technologies. The method involves acquiring continuous video sequences using a monocular camera, detecting the learner's facial region, and extracting the two-dimensional coordinates of facial feature points. Based on these two-dimensional coordinates, topological persistence features are analyzed to determine the distance change state. When a distance change occurs, the motion trajectory of the facial feature points is constructed, and the curvature distribution is calculated. Curvature entropy features are obtained through information entropy analysis. Consistency judgment is performed in conjunction with the topological persistence features, and the estimated distance change is adaptively corrected. Finally, the projection model parameters of the three-dimensional head pose estimation algorithm are dynamically adjusted based on the distance change, and the head pose angle is calculated by combining the facial feature point coordinates, thus achieving accurate judgment of the learner's attention state.

The invention relates to a skeleton point-based shielding scene behavior recognition method and system, and the method comprises the steps: firstly extracting time sequence skeleton point data through a human body detection and posture estimation algorithm, and constructing five types of structured inputs through a mask module; then, the multi-stream GCN learns local spatio-temporal features of different body areas respectively; in the fusion process, the model can adaptively reinforce the feature contribution of a region which is highly related to the current action or is not shielded, and meanwhile, the interference caused by a weak correlation region and a missing part is effectively inhibited. Meanwhile, three types of regularization items of diversity, sparsity and consistency are designed, so that the weights are kept different among the types, are kept compressed on branches and are kept stable in the types. The scheme provided by the invention is obviously superior to the existing method in complex scenes such as random shielding, arm shielding, leg shielding and the like, has high robustness and strong generalization ability, and is suitable for the fields of intelligent monitoring, human-computer interaction, edge calculation behavior analysis and the like.

The invention discloses a skeleton action recognition method based on a partition space-time collaborative graph convolutional network, and relates to the technical field of computer vision and pattern recognition, and the method comprises the steps: firstly obtaining human skeleton sequence data through a depth sensor or a posture estimation algorithm, and carrying out the preprocessing; then, constructing a partition association graph, dividing part partitions based on a human body natural structure, and establishing in-part stable topology and cross-part semantic topology; then introducing a part dynamic collaborative convolution (PDC-GC) module, and cooperatively extracting global semantic and local dynamic features of the action through part association modeling and dynamic neighborhood convolution; introducing a space-time sub-domain coding (STSE) module, capturing multi-scale space-time dependence by dividing four sub-domains, and reducing calculation complexity in combination with sparse coding; and finally, constructing a partitioned space-time collaborative graph convolutional network (PC-GCN) network, fusing a PDC-GC module, an STSE module and multi-level features, and realizing high-precision skeleton action recognition.

This invention discloses a method and system for deep learning-based human behavior recognition using ZYNQ, comprising the following steps: 1) acquiring a video dataset of human behavior; 2) performing data preprocessing, processing the human estimation information collected from the video dataset in step 1) using a human pose estimation algorithm, and dividing the collected data into a training set and a test set; 3) constructing a human behavior recognition model, using a designed deep learning neural network to extract and process features from the dataset in step 2), thereby training the model; 4) setting training parameters and training the designed deep learning network; 5) according to actual needs, using ZYNQ as the core platform, transferring the trained model, and finally deploying it to achieve online human behavior recognition. This invention is beneficial for recognizing complex, long-term reasoning behaviors in video scenarios and has practical application value.

The application relates to a lightweight robust face alignment method and system based on multi-task learning, which comprises the following steps: S1, collecting face images, and performing translation, scaling and rotation preprocessing on the face images to obtain a training set; S2, obtaining face key points L of the face images in the training set; using a pose estimation algorithm to mark 3D head pose Euler angles Theta of the face images in the training set, and converting the 3D head pose Euler angles Theta into a head pose rotation matrix R Θ ; S3, inputting the training set into a face alignment network to output face key point prediction values P and head pose rotation matrix prediction values R Φ , and respectively constructing a head pose loss function, a face key point loss function and a total loss function for training the face alignment network. The method provided by the application simultaneously aligns face key points and head poses using a lightweight model, and uses a head pose alignment task to assist the positioning of the face key points, thereby enhancing the robustness of the lightweight model to large-pose faces.

The application relates to the technical field of intelligent unmanned cluster systems, and discloses a cooperative positioning method for an unmanned cluster in a denial environment, which considers any pair of neighboring individuals except for both of which having no relative measurement capability, constructs observation equations among relative measurement information of both parties, displacement measurement information of both parties' odometers and relative poses of both parties, introduces a data-driven adaptive estimation technology, breaks through the deficiency of traditional relative pose estimation algorithms of unmanned systems that require robots to keep sustained excitation relative motion, thereby causing the control performance to be degraded, designs a distributed pose coupling estimator to realize cooperative estimation of relative poses of a leader individual by any individual in the cluster, realizes cooperative pose estimation of the unmanned cluster system under a weakly connected measurement topological condition, and solves the problem that existing cooperative positioning methods have poor scalability due to high requirements on measurement topologies.

The invention discloses a quay crane pose estimation fault tolerance method based on multi-sensor fusion, and belongs to the technical field of port automation equipment. According to the invention, when a quay crane is disconnected due to a network fault, the system compares and analyzes the pre-registered quay crane ID and quay crane placement sequence information with real-time received data, and automatically detects the pose disconnection condition. When a missing quay crane position is detected, a system immediately reports an error and records disconnection information, and different intelligent inference strategies are adopted based on the position sequence of the quay cranes in an area: for the quay crane in the middle position, an intermediate value of front and back adjacent quay cranes is used as an initial pose; for the quay cranes at the edge positions, the approximate poses are inferred based on the positions and overall trends of the adjacent quay cranes. According to the method, a pose estimation algorithm based on point cloud matching is adopted, high-precision relative pose estimation is realized through multi-sensor fusion of the laser radar and the visual camera, and the fault-tolerant capability and operation continuity of a port automation system are remarkably improved.

This invention provides a method, apparatus, and related equipment for driving a virtual human. The method includes: receiving a video to be identified; identifying the video using a target 2D pose estimation model and a target 3D pose estimation model to obtain the coordinates of multiple human body 3D key points; calculating the rotation angles of all frames in the video based on the coordinates of the multiple human body 3D key points; storing the coordinates of the multiple human body 3D key points and the rotation angles as a preset format file according to the parent-child relationship between the coordinates of the multiple human body 3D key points; and inputting the preset format file into a preset engine to drive a virtual human model in the preset engine. Based on the pose estimation algorithm, the optimized target 2D pose estimation model and target 3D pose estimation model are used to extract the coordinates of multiple human body 3D key points in the video to be identified, improving the speed and accuracy of pose estimation. Using the pose estimation algorithm to drive the virtual human model reduces production costs and facilitates the expansion of application scope.

The application provides an object pose estimation method and system based on three-dimensional feature mapping and perspective aggregation, and belongs to the technical field of pose estimation.The method is applied to a system comprising an image processing module, a three-dimensional feature mapping module, a perspective aggregation module and a pose estimation module, and specifically comprises the following steps: performing feature extraction on a template image and a target image, performing three-dimensional space mapping on the template image features, determining the template three-dimensional features and perspective information, determining the perspective information of each target image feature and the corresponding template three-dimensional features through feature matching, obtaining the matching relationship between the target image features and the template three-dimensional features through perspective aggregation, selecting a pose estimation algorithm to solve an alternative rough pose estimation result, and iteratively optimizing the initial value of a pose optimization network to obtain an object pose estimation result.The application can effectively reduce the error transmission risk in pose estimation, guarantee the accuracy, reduce the calculation complexity and improve the estimation efficiency.

The invention discloses an improved YOLOv8n-pose lightweight human body pose estimation method for solving the problems that an existing pose estimation algorithm is large in calculated amount, low in detection speed and the like, and aims at solving the problems that an existing model is poor in adaptability to small target detection and pose change, high in computing resource consumption and the like. The method comprises the following specific steps: firstly, acquiring a real-time video stream by deploying a camera, and performing down-sampling to extract a series of single-frame images; secondly, a human body detection model is used for obtaining a large number of human body pictures from video frames, joint point labeling is conducted on human bodies, and then the human bodies are divided into a training set, a verification set and a test set according to the proportion; thirdly, inputting the training set into a human body posture model for training to obtain a pre-trained human body posture estimation model; and finally, analyzing an input human body picture through the model, and accurately obtaining a key point position of each human body target. According to the attitude estimation method, a GhostNet module is introduced into a backbone network, so that the model parameter quantity and the calculation cost are remarkably reduced; an OREPA online convolution re-parameterization strategy is adopted, so that the training efficiency is improved; a task alignment dynamic detection head TADDH is designed, interaction between classification and positioning tasks is enhanced, and multi-scale target adaptability and positioning precision are improved. According to the lightweight structure, the model can be efficiently deployed in resource-limited equipment, and meanwhile, the model is excellent in detection of multiple persons, shielding and small targets in complex scenes, and has a wide application prospect.

This invention provides an automated operation system and method based on the collaboration of unmanned vehicles and tower cranes. The method involves a cloud platform issuing tasks; the unmanned vehicle guides its robotic arm to dynamically and precisely hook up a lifting device using a visual spatial pose estimation algorithm and Kalman filter trajectory prediction; the tower crane generates a global safe path based on a building information model and potential energy value, and performs dynamic obstacle avoidance using nonlinear model predictive control; simultaneously, a nonlinear feedback control law based on Lyapunov functions is used to dynamically fine-tune the mechanism speed to achieve active anti-sway compensation; finally, a blockchain subsystem generates a dynamic root hash value from the multi-dimensional interactive data and packages it onto the blockchain. This invention achieves seamless collaboration between ground and air equipment, completely eliminating the risks of manual hooking at heights, and greatly improving the safety, stability, and data traceability reliability of hoisting operations.

The application discloses a 3D pose optimization method based on translation and rotation double-branch time sequence filtering, belongs to the field of three-dimensional target detection and pose estimation, and comprises the following steps: calling a 2D-to-3D promotion module to obtain original 3D pose data of an object, and constructing a parallel processing translation filtering branch and a rotation filtering branch; the translation branch adopts exponential smoothing filtering to perform time sequence smoothing on a translation vector, and the rotation branch performs filtering processing on a rotation matrix by matrix and quaternion conversion and in combination with spherical linear interpolation; a time sequence cache is used to store a previous frame filtering result, a history reference is provided for current frame calculation, the cache is updated for next frame processing, and finally, a filtered translation vector and a rotation matrix are combined to reconstruct and output an optimized 3D bounding box. The application effectively suppresses position jitter and pose jump in time sequence output of a lightweight pose estimation algorithm, improves the stability and accuracy of three-dimensional pose estimation under the premise of maintaining high real-time performance.