109 results about "Fast motion" patented technology

The invention provides a multi-target tracking algorithm of a wide-view-angle feature fusion memory network, and belongs to the technical field of computer vision and artificial intelligence. The technical problem that the tracking performance is insufficient when a long-distance small-size target and a fast moving target are processed under the view angle of the unmanned aerial vehicle is solved. According to the technical scheme, the method comprises the following steps: S1, video frame preprocessing and target detection; s2, carrying out deep fusion on the picture feature information in a wide-view fusion memory network; s3, track confidence modeling is introduced in the filtering process of adaptive motion filtering AMF; and S4, in the first step of association, performing first association through a motion feature extraction model MFEM. The method has the advantages that the target position is adjusted and optimized step by step through dynamic updating of a tracking algorithm, and finally information of all tracks of the target is obtained.

The invention relates to the technical field of computer vision, and discloses a video dynamic target three-dimensional reconstruction method based on artificial intelligence, which comprises the following steps: step 1, image optical flow calculation: obtaining each frame of image in a video, calculating the optical flow value of each pixel point in each frame of image by extracting the spatial gradient of each pixel point in the image and the motion information of a target, and calculating the optical flow value of each pixel point in each frame of image; by analyzing the optical flow value, the displacement and the movement track of the target between the video frames are obtained; and step 2, optical flow weighting processing: on the basis of optical flow calculation, weighting processing is carried out on the optical flow value of each pixel. Weighing processing is introduced into image optical flow calculation, weighting factors are adjusted according to the movement speed and angle of the dynamic target, the precision of depth estimation of the dynamic target under the fast movement and complex background is improved, accurate depth data are obtained, depth information is provided for subsequent three-dimensional reconstruction, and the accuracy of depth estimation of the dynamic target is improved. The common error problem in dynamic target depth estimation is solved.

The invention provides a target tracking method and device for a fast moving small target, and the method comprises the steps: monitoring the position change of a target in continuous frames of an original target tracking data set, and estimating the motion speed of the target through calculating the difference of the positions of the target of adjacent frames; dynamically adjusting the sampling probability of the video frame according to the target movement speed; according to the adjusted sampling probability of each frame, probability resampling is carried out on the original target tracking data set, and a fast moving target tracking training set is generated; determining hyper-parameters of a speed sensitive loss function based on image features of the fast moving target tracking training set; and performing iterative optimization training of the target tracking model by using the fast motion target tracking training set and the speed sensitive loss function to obtain an optimized target tracking model suitable for the fast motion small target. The problems of inaccurate tracking, loss and the like in a high-speed moving small target tracking process in the prior art can be solved.

The invention relates to the technical field of image information processing, in particular to an image information processing method for short video production. According to the invention, each segment in a short video is analyzed and classified in real time by using a scene cognition algorithm; the scene cognition algorithm recognizes different scene types, and applies the most suitable image processing strategy for each scene; the definition of facial details and expressions is enhanced in a character close-up scene, the blurring and distortion of key points are reduced in a fast moving scene, and the optimal image quality is realized in each scene through dynamic processing; a digital twinning technology is introduced, a virtual model based on digital twinning is constructed, and virtual elements generated by AI are seamlessly integrated into an actual scene; physical characteristics of illumination, shadow, material reflection and the like in a real scene are simulated in real time by using a digital twin model, so that fusion between virtual content and a real background is more natural; the problem of image information processing in current short video production is solved.

The invention discloses an electric power cross-operation dynamic risk prediction and early warning method, system, equipment and medium based on multi-modal data fusion, and relates to the technical field of intelligent electric power systems, and the method comprises the steps: collecting multi-source data of a cross-operation site in real time, including personnel spatial position data, equipment state data, environmental parameters and operation flow information; performing multi-modal fusion processing on the multi-source data, and constructing space-time risk association features; constructing a dynamic risk factor matrix, performing fusion calculation on the static risk reference value and the dynamic correction value, and dynamically updating the weight of each factor along with time; predicting a risk change trend based on the combination of a time sequence prediction model and a graph structure modeling method; and generating a three-dimensional visual risk thermodynamic diagram based on a prediction result, and triggering a multi-level early warning mechanism when a risk value exceeds a threshold value. The image is reconstructed through multi-modal fusion processing, and key edge features can be recovered in complex environments such as low illumination, high dynamic range and fast motion.

The invention relates to the field of image processing, and discloses a video image segmentation method which is used for improving the precision, robustness and real-time performance of video image segmentation in a complex dynamic scene. The video image segmentation method comprises the following steps: generating an entropy generation rate map through weighted combination of light flow divergence and rotation, and quantifying motion irreversibility; a double-virtual-form prime concentration field is constructed, next-frame texture prediction is realized through iterative evolution, and the dynamic background adaptability is enhanced; fusing the projection entropy generation rate graph and the enhanced texture residual graph, dynamically distributing motion and appearance weights, and generating a high-precision boundary response graph; and extracting and persistent filtering are carried out, topological consistency maintenance of segmentation masks is realized, multi-scale collaborative segmentation and dynamic computing resource scheduling are supported, and efficiency and precision are balanced. The segmentation precision of the method is obviously superior to that of a traditional method in complex scenes such as illumination variation and rapid motion, and the method is suitable for the fields with high real-time requirements such as monitoring, medical treatment and automatic driving.

The invention discloses a visual displacement measuring method, which comprises the following steps of: utilizing a measuring camera which is only responsible for coordinate positioning of a target point; a correction camera and an IMU are utilized, and pose recognition of the correction camera is achieved through a vision-IMU fusion method; according to the pre-calibrated internal parameters of the two cameras and the external parameters between the two cameras, the pose change of the measurement camera is calculated and used for correcting the space coordinates of the target point, and target positioning deviation caused by vibration of the measurement camera is eliminated; and finally, calculating the displacement of the target according to the coordinate of the target point in the time domain by taking the initial moment as a reference. According to the method, the displacement resolution of the measured target is not sacrificed while the pose recognition of the measurement camera is ensured; and meanwhile, the robustness of visual pose recognition in complex environments such as rapid movement, complex illumination and shielding can be improved through assistance of inertial sensing.

The invention belongs to the technical field of computer vision, and discloses a target identification tracking method based on a self-supervision mechanism, and the method comprises the steps: enhancing a self-supervision pre-training module through causality, constructing a causal sample pair through unlabeled video data, learning universal features through combining with comparison loss, and achieving the high-precision tracking without large-scale manual labeling. A multi-modal feature fusion and dynamic calibration mechanism further reduces dependence on annotated data, is especially suitable for industrial inspection, field monitoring and other scenes where data acquisition is difficult, significantly reduces time and labor costs in a data preparation stage, and broadens the application range of the technology in resource limited scenes; a causal reasoning and physical constraint mechanism is introduced, a dynamic relation between targets is modeled through a space-time causal graph, unreasonable tracks are filtered in combination with a physical rule, and complex conditions such as shielding, rapid movement and extreme weather are effectively dealt with; the dynamic feature calibration module corrects feature drift in real time, and ensures stable model performance in long-term tracking.

The invention provides a multi-target tracking method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the target detection of a video sequence, and obtaining a detection frame of a current frame; under the condition that the detection frame of the current frame is successfully matched with the prediction frame, calculating a track consistency score; for the target trajectory of which the trajectory consistency score exceeds a set score, extracting feature representation of a historical frame by using a time sequence attention mechanism; and calculating feature similarity according to the feature representation and a set model, and determining the identity of the target trajectory. According to the method, the video frame is detected through the target detection technology, the corresponding high-confidence detection box is screened out, the purposes of accurately positioning multiple targets and reducing false detection can be achieved, and reliable input is provided for follow-up track association. The target position is predicted through the trajectory prediction technology, matching of a detection frame and a prediction frame is achieved, the purpose of maintaining trajectory continuity in a shielding or rapid motion scene is achieved, and the target identifier switching rate is remarkably reduced.

This invention discloses a target tracking method for unmanned aerial vehicles (UAVs), relating to the field of computer vision, aiming to solve the problems of target occlusion, rapid movement, and efficiency in dynamic scenes. The method includes: acquiring the target's bounding box and confidence score through a detector, and predicting the target's position and state using a noisy adaptive Kalman filter; proposing a confidence-based feature extraction and judgment strategy, performing IoU matching between the trajectory and the detection box; for targets with a matching degree below a threshold and a confidence score change rate above a threshold, extracting appearance features using a lightweight feature re-identification network (Rep-OSNet); otherwise, reusing features from the previous frame; designing a cost function that fuses motion direction and appearance similarity to perform cascaded matching between the target and the confirmed trajectory; performing secondary association matching on targets and trajectories that mismatch in the cascaded matching; updating the matched trajectory state using a noisy adaptive Kalman filter, deleting long-term lost mismatched trajectories, and outputting the target trajectory prediction box and ID.

The invention discloses a nanoscale high-precision positioning method based on a multi-degree-of-freedom motion workbench, and relates to the field of large-stroke and high-precision positioning, and the method comprises the steps: S1, initializing the motion workbench, and guaranteeing that a system is in a zero position state; s2, sending a target position instruction by the upper computer, and driving the macro moving platform to quickly move to a position near a target; s3, acquiring the current position, calculating the deviation from the target position according to the target displacement and the current actual feedback displacement value, and generating micropositioner control parameters; s4, a control instruction is generated, and nanoscale displacement compensation is executed; and S5, iteratively correcting the displacement of the micropositioner until the deviation fed back by the laser interferometer meets the requirement. According to the invention, the positioning precision of large-stroke (hundred millimeters) movement is improved to a nanometer level, the device is suitable for the fields of photoetching machines, ultra-precision machining, biological micromanipulation and the like, and the equipment performance and the process level are improved. In addition, the system adopts a modular design, can adapt to different driving and sensing units according to requirements, and has strong expansibility.

The invention belongs to the technical field of computer vision, and discloses a micro-expression recognition method based on a fast and slow motion double-branch network. Carrying out space normalization and time normalization processing on the micro-expression video clip, and extracting fast motion and slow motion from the micro-expression video clip; designing a fast and slow motion double-branch network to obtain fast motion features and slow motion features; the fast motion features and the slow motion features are fused in a feature addition mode, and fused features are obtained; and a classifier module is adopted to classify the fusion features, and micro-expression recognition is achieved. The fast motion branch focuses on overall motion, and long-term and overall motion information is captured; the slow motion branch focuses on detailed motion, captures short-term and detailed motion information, and obtains performance improvement. And designing a space-time modeling mechanism for adding accumulative attention, and adding an attention marking module at each stage to realize accumulative attention. In addition, the motion is represented by pixel difference, the implementation process is simplified, and the calculation complexity is reduced.

The invention discloses a space-time fringe analysis method based on physical prior guidance. The method comprises the following steps: firstly, acquiring a stripe sequence taking a target frame as a center, and separating background light through a background estimation network to obtain background light intensity; then, under the assumption of constant brightness and small displacement, an optical flow pixel alignment module is used for estimating inter-frame optical flow, and sub-pixel-level registration is carried out on the subsequences; and finally, inputting the alignment sequence into Attention U-Net with an attention mechanism to carry out space-time phase demodulation, and outputting a target frame phase. And then phase unwrapping is carried out, and three-dimensional reconstruction is completed in combination with system calibration parameters. In the training stage, a space-time consistency loss function and a frequency domain consistency loss function are introduced to improve the generalization ability. The method has high phase demodulation precision and time sequence stability in a low signal-to-noise ratio and fast motion scene, and is suitable for high-speed three-dimensional measurement engineering application.

The invention discloses a visual multi-mode non-contact gesture unlocking method. The method comprises the following steps: S1, collecting multi-source sensing data; s2, preprocessing the RGB image data; s3, the event stream and the RGB image are fused, the features of the fingers and the palm are used as vertexes, connecting lines are used as edges, and a hand space structure is expressed; constructing time correlation by taking a connecting line of the same vertexes in front and back continuous frames as an edge, and dynamically mapping an event stream into a graph structure to obtain a multi-modal fusion feature; s4, performing multi-modal joint prediction, mapping the multi-modal fusion feature to a neural network model of a potential space, outputting parameters mu and sigma of potential distribution, and generating a potential feature Z through a re-parameterization technique; and after the potential feature Z and the additional condition channel are overlapped, decoding is carried out through a multi-layer perceptron (MLP), and final hand posture prediction is obtained. The invention provides low-delay and high-robustness real-time gesture interaction and high-precision sign language recognition suitable for challenging environments such as illumination variation and fast movement.

The invention relates to the technical field of target tracking, in particular to a target loss and recapture method based on correlation filtering, and the method comprises the steps: judging the target loss and recapture conditions through a correlation filtering algorithm and weighted logic; when the target appears again, judging whether the scale changes or not, and then increasing or reducing the corresponding step length to change the scale; the method comprises the following steps of: carrying out target repositioning by adopting an SURF (Speeded Up Robust Features), carrying out target detection by using the extracted SURF after a locked target is lost, and when the target appears again, re-determining the position of the target through feature matching; when the target is about to be lost, template updating is suspended, key frame features are reserved, and a recognition algorithm or SURF feature matching is utilized to recapture the target position; and the next frame of video stream is read for continuous tracking, and if the video stream reading is finished or the tracking fails, the tracking is stopped, so that the technical problems of low tracking success rate and low tracking accuracy in the prior art under the complex conditions of a fast moving target, long-time shielding, continuous change of the target size and the like are solved.

The invention discloses an embedded firmware camera data modification method and device, a medium and equipment, and relates to the technical field of computer vision, and the method comprises the steps: collecting image data in real time, and carrying out the primary processing; a frame difference method and a background modeling algorithm are adopted to carry out region division and priority distribution on the image data after primary processing, and a region priority mapping table is generated; extracting feature points of a high-priority motion area in the optimized image data through an optical flow method, and calculating a motion vector between continuous frames; according to the calculated motion vector between the continuous frames, an event-driven frame processing mechanism is triggered, and the frame rate and the processing precision are automatically adjusted; through the motion vector extracted through the optical flow method, the motion speed of the object can be calculated in real time, the frame rate and the processing precision are dynamically adjusted according to the motion speed, it is ensured that enough details are captured in the rapid motion process, and processing resources are saved in the static process.

Despite recent progress, existing frame interpolation methods still struggle with extremely high resolution images and challenging cases such as repetitive textures, thin objects, and fast motion. To address these issues, provided is a cascaded diffusion frame interpolation approach that excels in these scenarios while achieving competitive performance on standard benchmarks.

The invention relates to an RGB-T target tracking method and system based on spatio-temporal state evolution, and the method comprises the steps: constructing a double-branch RGB-T target tracking model, enabling RGB and TIR branches to share the weight of a ViT encoder, employing an iterative processing frame, and transmitting spatio-temporal context information between frames through an updatable context memory Tokens. In each iteration, after RGB and TIR modal features are extracted respectively, cross-modal time sequence context modeling is carried out through a modal perception time sequence Mamba module, and the module realizes long-time target perception representation learning through a cross-modal coupling state transition mechanism and a prompt guide strategy; and carrying out intra-modal and inter-modal feature fusion in a spatial dimension through a cross-modal Mama aggregation module, and finally outputting a target position through a prediction head. According to the method, lasting cross-modal state evolution can be realized with linear complexity, the spatial-temporal characteristics of visible light and infrared light are effectively fused, and the tracking robustness and accuracy are improved in complex scenes such as rapid target movement, shielding or modal degradation.

This invention provides a motion estimation-based method for accelerating video recognition, comprising: identifying keyframes and non-keyframes in a video sequence; extracting features from keyframes using a Bayer domain basic model to obtain perceptual features; calculating motion vectors between non-keyframes and a reference frame (the frame preceding the non-keyframe) using a fast motion estimation module, wherein the fast motion estimation module employs a pyramid block structure and performs multi-level matching search from coarse to fine under a GPU parallel architecture; deforming the features of the reference frame using the motion vectors to obtain propagation features; predicting the perceptual residual of the current frame using a perceptual residual correction network, numerically correcting the propagation features using the residual, and outputting the corrected propagation features, wherein the perceptual residual correction network is a lightweight network structure; and performing video recognition based on the corrected propagation features. This invention achieves faster and more efficient video recognition.

The application discloses a handheld gimbal capable of carrying a long-focus shooting device, which comprises a handheld part, a yaw shaft assembly and a pitch shaft assembly arranged in layers; the yaw shaft assembly comprises a yaw shaft shell, a yaw shaft driving device installed in the yaw shaft shell and a yaw shaft motor base connected with the yaw shaft driving device, and the top of the yaw shaft motor base is connected with the pitch shaft assembly; the pitch shaft assembly comprises a shaft arm fixedly connected with the yaw shaft motor base, a pitch shaft shell rotationally connected with the shaft arm, a pitch shaft driving device installed in the pitch shaft shell and fixedly connected with the shaft arm, a harmonic reducer connected with the output end of the pitch shaft driving device and fixedly connected with the pitch shaft shell; and the pitch shaft assembly further comprises a quick release part fixedly connected with the pitch shaft shell. The application has the beneficial effect that the "visual target detection / tracking" and the "high-precision execution structure of the two-axis gimbal" are integrated in a closed loop, so that the camera can keep stable and robust in tracking under the condition of rapid movement / occlusion.

The invention discloses a motion scene human body posture estimation method driven by adaptive time modeling and a medium, and the method comprises the steps: constructing an adaptive time modeling motion scene 3D human body posture estimation network model based on the motion speed degree; the robustness of the model to a motion scene is improved by using motion prior based on physical constraints and predicting the uncertainty of 3D human body articulation points; human body motion videos with different motion speeds are used as input, a corresponding 2D human body joint point sequence is obtained on the basis of an existing high-quality 2D human body posture estimation model, the 2D human body joint point sequence is input into the network model, and a trained model is obtained; according to the method, the receptive field can be adaptively adjusted according to the movement speed, the small receptive field is used for capturing transient changes for fast movement, the large receptive field is used for capturing long-term dependence for slow movement, the method adapts to movement at different speeds, and therefore the human body posture estimation accuracy in movement scenes at different speeds is improved.

The present invention is a correlation filtering tracking method based on relative entropy, comprising step 1: reading the position information of the first frame image of the video and the target to be tracked, and adding them to the training set; step 2: extracting the features in the current training set, training the correlation filter, initializing, and fitting the response graph using the relative entropy measurement method; step 3: extracting the image block for target detection with the target position of the previous frame as the center, and extracting features for the candidate samples of the current frame image; step 4: model optimization, generating an optimized and updated correlation filter based on relative entropy; step 5: making the optimized and updated correlation filter and the candidate image block perform convolution operation in the frequency domain, and the obtained result is converted to the response graph of the time domain by inverse Fourier transform; step 6: repeating steps 3-5 until all frames of the video are read. The method has high recognition accuracy and can stably track targets in complex environments such as illumination changes, target occlusion, deformation and rapid motion.

The video resolution enhancement system and method based on the pre-trained video generation model relates to the field of image processing technology, including: S1, obtaining the low-resolution video to be processed and its related text description and pre-processing through the pre-processing module; S2, processing the pre-processed low-resolution video and its related text description through the pre-trained video generation model to obtain high-level features; S3, extracting additional visual features from the low-resolution video through a feature extractor; S4, inputting the high-level features and the additional visual features into the super-resolution module to obtain a preliminary high-resolution video; S5, inputting the high-level features and the preliminary super-resolution image into the fusion module to obtain the final high-resolution video. The present invention not only effectively improves the spatial resolution of the video, but also maintains the temporal consistency and semantic integrity of the video, can handle various complex scenes and dynamic content, and overcomes the limitations of traditional methods in processing fast motion and complex textures.

A method for machine tool motion control determines a time-optimal trajectory for multi-segment tool path motion. Start and end waypoints are defined for each segment of the overall motion, as well as other conditions such as cutting feed speed. An initial motion profile for each segment is calculated based on waypoint geometry and other constraints, and motion states at waypoints connecting the segments are optimized to provide the shortest total trajectory time. The optimized waypoint states include speeds and accelerations having non-zero values. Additional waypoints may be added to the trajectory, such as for collision avoidance, and again the waypoint state is optimized for the minimum total time of the complete trajectory. And calculating an optimal waypoint state value by using a heuristic and gradient descent technology.

The application discloses a motion training evaluation system and method based on cooperation of an event camera and a visual camera, and is applied to the field of computer vision, and aims at the problems that an existing training evaluation system based on an optical camera cannot capture fast motion trajectories and limb shapes, cannot effectively evaluate action quality, and cannot provide effective training guidance; the application provides continuous human motion events in the form of event streams, can achieve millisecond-level motion response, is not affected by motion blur effects of high-speed moving objects, and can provide a higher dynamic range, and can provide more effective motion training evaluation in scenes with strong light, backlight, and sharp changes in brightness. Through cooperation of the visual camera and the event camera, not only the accuracy of fast actions can be evaluated, but also standard action teaching and training in the form of visual scene interaction can be performed.

This invention belongs to the field of virtual reality technology, specifically relating to an XR virtual-real interaction control device that integrates optical motion capture and inertial navigation. It includes: a metasurface polarization-encoded marker array, distributed and installed at preset tracking point positions on the user's hand and head, used to generate reflected light carrying polarization identification codes when receiving illumination light; a miniature inertial measurement unit array, rigidly connected to each metasurface polarization-encoded marker in the metasurface polarization-encoded marker array, used to collect acceleration and angular velocity data at each tracking point; and a near-eye display device, including an illumination module and a polarization camera module. The illumination module projects linearly polarized illumination light onto the user's hand and head areas, and the polarization camera module receives the reflected light generated by the metasurface polarization-encoded marker array and outputs multi-channel polarization images. This invention achieves stable tracking in occlusion and rapid motion scenarios through tightly coupled visual-inertial fusion.

The invention relates to a whole body attitude estimation method and system based on a downward fisheye and an enhanced EgoPoseFormer model, and the method enlarges the human body capture range through the layout of the bottom visual angle of a head-mounted display, compensates geometric nonlinearity in a feature extraction stage through distortion perception convolution, cooperates with an attention mechanism of an embedded pose compensation item, and achieves the estimation of the whole body attitude. And the feature space consistency is maintained when the camera dynamically shakes. Meanwhile, in combination with cross-frame time sequence gating and a multi-modal filtering algorithm, inertial data is utilized to perform kinematics correction on a visual predicted value, and smooth track output is realized in a shielding or rapid motion scene. According to the scheme, the computing power overhead is reduced through model quantification and operator fusion, and low-delay real-time whole body attitude tracking is realized in a mobile terminal chip environment.

This invention discloses an embedded firmware camera data modification method, apparatus, medium, and device, relating to the field of computer vision technology. The method includes: real-time acquisition of image data and preliminary processing; using frame difference and background modeling algorithms to divide the pre-processed image data into regions and assign priorities, generating a region priority mapping table; extracting feature points of high-priority motion regions in the optimized image data using optical flow, and calculating motion vectors between consecutive frames; triggering an event-driven frame processing mechanism based on the calculated motion vectors between consecutive frames, automatically adjusting the frame rate and processing accuracy; and using the motion vectors extracted by optical flow to calculate the object's motion speed in real time, dynamically adjusting the frame rate and processing accuracy according to the motion speed, ensuring sufficient detail is captured during rapid motion while saving processing resources when stationary.

The invention relates to the technical field of machine vision, and discloses a cable diameter detection method and system based on machine vision. According to the method and system, spatial-temporal feature modeling is carried out by introducing the pulse neural network, cable images can be captured and analyzed in real time under the conditions of complex illumination and motion blur, it is ensured that the error of cable diameter measurement is controlled within the high-precision range smaller than or equal to 0.05 mm, and the measurement accuracy is improved. The design of a shielding compensation mechanism effectively solves the problem of image missing caused by rapid movement and a shielding object, so that the detection reliability is further improved, and the embedded design of the system not only reduces the power consumption by 40%, but also enables the system to adapt to a high-speed production environment, and meets the requirements of the modern industry for automation and intelligence.

The invention discloses a fast motion video image feedback system based on a cloud network. The fast motion video image feedback system comprises a camera, a wireless transceiver, a controller, a motion recognition module, a motion monitoring module, a multi-algorithm program module, a cloud database and a motion real-time feedback module. The camera is used for collecting videos and importing shot videos into the motion recognition module through the wireless transceiver, the wireless transceiver is used for transmitting the videos and data in real time, the controller is used for processing and analyzing video images, and the motion recognition module comprises a cloud network and a cloud storage unit. The motion monitoring module is used for comparing or tracking technical parameters of athletes, the motion real-time feedback module is used for tracking historical data of the athletes or comparing the historical data with related kinematics parameters of world-level excellent athletes to generate an intelligent analysis report, and the intelligent analysis report is fed back to the equipment through the wireless transceiver. The method has the advantages of high efficiency, real-time performance, accuracy, safety and the like.