83 results about "Motion intensity" patented technology

A dynamic adaptive depth camera occlusion detection method, system and device, and a storage medium, the method comprising: step T1: constructing a three-frame time sequence analysis window, continuously obtaining infrared speckle image sequences at different moments and corresponding dense depth map sequences, and establishing a pixel-level space-time alignment mapping relationship; t2, calculating a motion vector between adjacent frames, and executing the step T3 when the average motion intensity exceeds a dynamic threshold value; wherein the dynamic threshold is determined by the dense depth map; t3, positioning a speckle feature point in the infrared speckle image, generating an analysis window by taking the speckle feature point as a center, calculating a gradient change rate, and when the gradient change rate is smaller than a gradient threshold value, marking the region as a primary candidate region; and T4, carrying out space-time consistency comparison on the primary candidate region and two adjacent frames, and reserving a shielding region of which the variation value is smaller than a variation threshold value in three continuous frames. The method is high in precision, good in real-time performance and high in stability.

The invention relates to the technical field of camera anti-shake, and discloses a motion camera anti-shake method and system based on inertial prediction, and the method comprises the steps: collecting the three-axis acceleration original data of an IMU sensor in a motion camera, and calculating a first motion intensity value; when the first motion intensity value is smaller than an acceleration change rate threshold value, starting a first anti-shake mode, and extracting angular velocity data of the gyroscope; performing cubic spline interpolation curve fitting and time domain extrapolation prediction on the angular velocity data of the gyroscope to obtain a three-axis angular displacement predicted value and generate a first lens compensation driving signal in a first anti-shake mode; continuously monitoring the motion camera, switching to a second anti-shake mode when a second motion intensity value exceeds the acceleration change rate threshold value, acquiring three-axis acceleration sudden change data, and generating a second lens compensation driving signal in the second anti-shake mode, compared with a traditional single-mode anti-shake method, the method improves the adaptability and compensation precision in a complex motion environment.

The invention is suitable for the technical field of 3D scanning, and provides a figure model 3D scanning and dynamic attitude capturing method and system, which dynamically triggers 3D scanning by monitoring joint movement intensity in real time, continuously collects continuous movement tracks by using an inertial sensor in a non-scanning period, and reversely deduces space-time parameters of a shielded part through multi-light-source shadow boundary evolution. And constructing a spatio-temporal joint evaluation function to adaptively adjust the scanning frequency and the sampling rate, and finally performing spatio-temporal alignment and confidence weighted fusion on the discrete point cloud, the continuous trajectory and the reverse reckoning data to generate a dynamic biomechanical model. The scheme breaks through the bottleneck of resource waste and blind area shielding of traditional fixed frequency scanning, realizes space-time complementation and precision optimization of motion monitoring, and is suitable for dynamic scenes such as movie and television animation, medical rehabilitation, physical training and the like. According to the system, through multi-modal data fusion and self-adaptive resource scheduling, the data integrity, the real-time performance and the biomechanical analysis capability are remarkably improved, and an efficient solution is provided for human motion digitization.

The invention provides an unsupervised video target segmentation method based on motion-appearance multi-level clustering. The method comprises the following steps: inputting a video frame into a backbone network to extract motion features; designing a position-channel attention module to enhance spatio-temporal context modeling; motion and appearance features are fused through a multi-level clustering refinement strategy; a reconstructed optical flow is used as a motion intensity indicator, and the complex scene processing capability is improved by combining subspace clustering multi-view analysis; and finally outputting the target segmentation mask. According to the method, the discriminative motion features can be extracted from the FlowFormer + + optical flow network and the position-channel attention module, and the motion features and the appearance features in the Vision Transform backbone network can be effectively fused by the proposed multi-level clustering refinement strategy; according to subspace clustering multi-view analysis designed by the invention, larger weights in features can be concentrated in a foreground target area, useless background information is filtered out, and compared with an existing unsupervised video target segmentation method, the method has higher segmentation precision.

A device may obtain an indication of motion associated with a video block. The device may determine a precision level of a motion vector difference associated with the video block based on the indication of motion associated with the video block. The device may decode the motion vector difference associated with the video block using the precision level, for example, if the device is a decoder. The indication of the motion may include one or more of a value of a motion vector predictor (MVP), motion information e.g. motion intensity, or depth information, which indicates a distance associated with the video block from a reference point.

The invention discloses a ViT model lightweight method for video action recognition, which comprises the following steps of: performing motion estimation in a data processing stage, calculating to obtain motion intensity and time redundancy scores of tokens, and performing window division on video frames according to the scores. Afterwards, a window-global token merging strategy is adopted to effectively merge redundant tokens, the first three layers of the model process local information through adaptive window merging, and from the fourth layer, the model repeatedly executes global merging according to the depth of the layers so as to realize gradually enhanced feature representation; therefore, the number of tokens is reduced, the complexity of the ViT model in space-time self-attention calculation is reduced, and the problem of high calculation resource consumption caused by space-time information redundancy of the current ViT model in a video task is solved. Calculation overhead and energy consumption can be remarkably reduced in tasks such as video action recognition, action detection and time sequence event understanding, and meanwhile the characterization capacity of a key dynamic area is kept.

This invention pertains to the field of mobile device authentication and discloses a dynamic context-aware continuous authentication method for mobile devices. It constructs a two-dimensional behavior quantizer based on motion intensity and interaction density to define the physical boundaries of pure motion, pure interaction, and hybrid scenarios in real time. A gating unit controlled by MI / ID signals is introduced to automatically generate masks in extreme scenarios such as pure motion or pure interaction to suppress ineffective attention calculations for missing modalities, enabling full feature interaction only in hybrid scenarios. A deterministic mapping rule from physical context state to network sparsity is established. Based on the real-time calculated MI / ID physical metrics, the system directly indexes the corresponding sparse sub-paths in the pre-trained multi-pruning rate network, achieving millisecond-level zero-overhead state switching while ensuring security.

The invention particularly relates to a self-adaptive closed-loop brain-computer interface neural feedback training system, and relates to the technical field of brain-computer interfaces and neural feedback. A multi-modal feature extraction module; a coefficient fusion module; and a feedback training module. In the invention, a high-precision crystal oscillator clock is adopted to realize time synchronization of electroencephalogram, eye movement and behavior signals, fusion distortion caused by signal dislocation is thoroughly eliminated, and the three types of signals respectively cover cognitive states, visual attention and motion characteristics to form complementary state evaluation dimensions; a refined quantization algorithm is designed for each mode, wherein instantaneous artifacts are eliminated through extreme value screening of the electroencephalogram coefficient, the pixel diameter is calibrated into the physical diameter through the eye movement coefficient so as to eliminate imaging interference, and the large-amplitude movement intensity and high-frequency posture micro change are considered in the behavior coefficient.

The application relates to the technical field of image recognition, in particular to a children autism spectrum disorder stereotyped behavior recognition system based on video images. The system screens target pixel points from pixel points; determines a motion intensity index according to a trajectory point sequence of the target pixel points; converts the trajectory point sequence of the target pixel points into a motion vector sequence; determines direction similarity according to direction similarity between vectors; clusters the vectors based on the direction similarity, and identifies a target cluster; determines a motion regularity index according to the number of vectors in the target cluster and the direction similarity; determines a saliency index in combination with the motion intensity index; clusters the target pixel points based on the saliency index, and sets a region of interest; determines an enhancement weight according to the density of the target pixel points in the region and the saliency index; and determines a total enhancement demand value in combination with the gray scale and gradient features of the pixel points in the region; and reversely maps the total enhancement demand value into a gamma correction parameter, so that the accuracy of recognition is improved.

The embodiment of the invention provides a video processing method and device, equipment and a medium. The method comprises the steps of determining a scene type corresponding to a to-be-processed video, and determining a target feature extraction strategy of the to-be-processed video based on the scene type; determining a motion feature corresponding to the to-be-processed video based on the target feature extraction strategy, and determining the motion intensity of the to-be-processed video based on the motion feature; determining a filtering parameter of the to-be-processed video based on the motion intensity, and filtering the to-be-processed video based on the filtering parameter to obtain filtered video data; and determining a spatial transformation parameter corresponding to the to-be-processed video based on the filtered video data, and performing inter-frame transformation on the to-be-processed video based on the spatial transformation parameter to obtain a stable video sequence. The method is used for achieving the effect of effectively improving the stability and the visual quality of the video in a complex dynamic scene.

The application relates to the fields of edge computing and video analysis, and discloses an edge video perception analysis method and system based on codec metadata, specifically, a terminal-side device extracts physical features, target scale, texture entropy and motion intensity, from a video, and pre-prunes a search space of resolution, code rate and frame rate; an edge device constructs an explicit analysis mapping model, performs fine-grained dynamic precision prediction, and solves an online optimization problem to lock an optimal configuration; the terminal-side device executes the optimal configuration, a video stream is uploaded to the edge device for inference tracking, and inference results and update states are fed back to the terminal-side device, thereby forming a millisecond-level adaptive closed-loop control; the application perceives video content features in an approximate zero-cost manner, and establishes a deterministic physical constraint between the features and the configuration, so that resource scheduling can be quickly and accurately guided.

The present application discloses a motion detection method and apparatus, the method is based on a wearable device, the method comprising: under the condition that the user is in an effective motion state, according to the vital sign data of the user in the effective motion state, determining the first motion level corresponding to the effective motion state; and, according to the motion data of the user in the effective motion state, determining the second motion level corresponding to the effective motion state; the first motion level and / or the second motion level at least show: the motion intensity when the user is in the effective motion state; according to the first motion level and the second motion level, obtaining the motion status information of the user in the effective motion state. It can be seen that the motion detection process in this specification comprehensively determines the motion status information of the user based on the first motion level and the second motion level and based on different dimensions, so that the obtained motion status information is more objective and can more accurately characterize the user's motion status.

This application relates to methods for operating a multi-modal sensor device and associated apparatus. The invention provides a BMD that has multiple device modes depending on the operating conditions of the device, such as motion intensity, device placement, and / or activity type, which are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods are provided for tracking physiological metrics using the BMD. In some embodiments, the process and the BMD apply time-domain analysis to data provided by the sensors of the BMD when the data has high signal (e.g., high signal-to-noise ratio), and apply frequency-domain analysis to the data when the data has low signal, which facilitates improved accuracy and speed of biometric data.

A system (100) for real-time video enhancement using diffusion-based AI models, consisting of: a video input processing module configured to receive and preprocess video images by performing noise reduction, image alignment, and dynamic content analysis; a diffusion-based AI enhancement module configured to apply iterative noise-to-image transformations to refine video images, restore lost details, improve resolution, and correct color distortions; an adaptive parameter tuning module configured to dynamically adjust enhancement parameters based on real-time video characteristics such as motion intensity, lighting conditions, and noise levels; a real-time optimization module configured to utilize model compression, edge computing, and parallel processing to reduce computational complexity and achieve low-latency processing; a motion stabilization module configured to detect and compensate for motion artifacts and flicker effects to ensure smooth video playback; an output / playback module configured to process and reconstruct the enhanced video images for real-time display, storage, or transmission over digital networks.

The application discloses a video key information extraction method, a video question and answer method, equipment and a medium. The video key information extraction method comprises the following steps: calculating the degree of motion intensity of pixels in a video frame sequence to obtain a motion amplitude value corresponding to each video frame in the video frame sequence; calculating a clustering weight corresponding to each video frame based on the motion amplitude value corresponding to each video frame; performing weighted clustering on each video frame based on the clustering weight corresponding to each video frame, and taking a video frame corresponding to a cluster center point of each cluster as a centroid video frame; selecting a video frame with an image similarity between each centroid video frame satisfying a preset condition to obtain a key video frame; and performing image information extraction on each key video frame to obtain key information corresponding to the video frame sequence. Clustering can better process video frames with different importance, obtain more accurate and reliable centroid video frames, and then more accurate key information can be extracted.

The invention provides a multi-scene disaster risk early warning method and device fusing multi-source vision and deep learning and a storage medium, and relates to the field of disaster early warning, and the method comprises the steps: calculating the motion intensity of each image frame in a fused video; obtaining a dynamic intensity threshold value of each time window; based on the motion intensity of each image frame in each time window and the dynamic intensity threshold value of the corresponding time window, performing cutting extraction on the corresponding fusion video of each time window, and obtaining a video clip for dividing a scene boundary; inputting the obtained video clips into a pre-trained disaster classification model to obtain disaster category vectors; inputting the fused video and the disaster category vector into a pre-trained disaster risk identification model to obtain a disaster category result and disaster risk possibility data; and based on the disaster category result and the disaster risk possibility data, performing disaster early warning in combination with disaster risk grading. By adopting the early warning method provided by the invention, rapid and efficient early warning of disasters can be realized.

The invention discloses a landscape lighting intelligent adjusting system based on tone and motion state fusion analysis, and relates to the technical field of digital information processing. The method is used for solving the problems of multi-modal data logic conflict and control signal step. The method comprises the following steps: firstly, executing frequency domain transformation and differential operation on acquired space-time sequence data to generate normalized tone and motion intensity; then, calculating a cooperative gain coefficient and a dispersion index, and quantifying a scene active level and a data deviation; then, calculating a credibility weight based on the dispersion, and executing fusion iterative operation on a preset parameter and a historical state to generate a target control parameter; and finally, generating a pulse width modulation digital sequence in smooth transition by using a linear interpolation algorithm, and outputting a control instruction. According to the system, high-reliability fusion of multi-source data and adaptive smoothing of lighting effect control are realized.

This invention discloses an adaptive temperature control method based on motion state perception, comprising the following steps: Step 1, the motion sensing module senses the user's motion intensity and transmits the motion intensity data to the main control module; Step 2, the main control module determines the motion intensity level based on the motion intensity data, matches a target heating level based on the motion intensity level, and sends a target heating level command to the heating module; Step 3, the heating module adjusts the temperature to the target temperature range according to the target heating level command. An adaptive temperature control system based on motion state perception is also disclosed, comprising a motion sensing module, a main control module, and a heating module; the temperature control system of this invention can automatically match the heating level temperature according to the motion intensity, so that the user does not need to manually adjust the temperature during exercise.

The invention discloses a vestibular eye reflex function detection and evaluation system and method. A control host analyzes and records the word size of each input letter E, the response time of a detected person and whether the opening direction of the letter E is accurately recognized or not when the detected person is in a static state; when the detected person is in N different exercise intensity states, analyzing and recording the font size of each letter E put in each exercise intensity state, the response time of the detected person and whether the opening direction of the letter E is accurately identified or not; the method comprises the following steps: analyzing the size of the minimum word size of the detected person in the opening direction of the letter E accurately identified in the static state and each motion intensity state, and respectively calculating the actual difference value of the size of the minimum word size of the detected person in the opening direction of the letter E accurately identified in each motion intensity state and the static state so as to obtain N actual difference values, f (x) = ax + b fitting is carried out on the N actual difference values to obtain a value a, and if the a value is larger than a set value, the vestibular eye reflex function of the detected person is damaged.

The invention provides a video coding intelligent code rate control method, electronic equipment and a storage medium, and relates to the technical field of video transmission. The method comprises the following steps: acquiring an image of a current GOP; determining the average code rate and the maximum code rate of the image of the current GOP; performing motion detection on the image of the current GOP to obtain a region of interest and motion intensity; and controlling the code rate according to the average code rate, the maximum code rate, the region of interest and the exercise intensity. According to the method, code rate features and motion detection information of GOP are fused, accurate, efficient and adaptive code rate adjustment can be realized, and the method can adapt to complex and changeable application scenes.

The application relates to the technical field of video content auditing, and discloses a video content auditing system based on a large model, which comprises the following steps: information such as the display order and the decoding order relationship of a coded video stream, a motion vector, a coded block spatial position and a video frame center position is extracted through a data acquisition unit; a local offset calculation unit is used to perform time domain difference operation on the motion intensity, and the time sequence offset caused by compression is quantized; a torque diagram generation unit is used to fuse the local offset, the direction symbol and the space vector torque, and construct a frame-level directed heterogeneous torque diagram; a feature processing unit is used to stack multiple torque diagrams into a three-dimensional feature input convolutional neural network, and output a time anchor point of an event occurrence; finally, an auditing execution unit determines an auditing window according to the time anchor point and a frame rate, intercepts an original video segment, and generates visual information to mark manual auditing.

The application discloses a rehabilitation training method and system based on a human motion model, relates to the technical field of rehabilitation training, and mainly has the following scheme: collecting multiple data of a patient; constructing a human motion model and outputting simulation data; calculating a physiological index coefficient, and further calculating the motion intensity of each training action; calculating the motion mode complexity index of each training action, and then obtaining the training duration of each training action; calculating a motion deviation value, and then calculating a comprehensive dynamic deviation index. A preset threshold value is used to determine whether the rehabilitation training plan is suitable for the current state of the patient, and whether the rehabilitation training plan needs to be changed. The method can evaluate the adaptability of the rehabilitation training plan in real time, dynamically adjust the training scheme, and ensure that the training always conforms to the state of the patient, thereby improving the rehabilitation efficiency and success rate.

The invention relates to the technical field of video content auditing, and discloses a video content auditing system based on a large model, which comprises the following steps of: extracting information such as a relationship between a display sequence and a decoding sequence of an encoded video stream, a motion vector, an encoded block space position and a video frame center position through a data acquisition unit; a local offset calculation unit is used for carrying out time domain differential operation on the motion intensity, and time sequence offset caused by compression is quantified; the local offset, the direction symbol and the space vector moment are fused through a torque diagram generation unit, and a frame-level directed disparity torque diagram is constructed; stacking multiple frames of torque diagrams into three-dimensional features by a feature processing unit, inputting the three-dimensional features into a convolutional neural network, and outputting a time anchor point when an event occurs; and finally, the auditing execution unit determines an auditing window according to the time anchor point and the frame rate, intercepts the original video clip and generates visual information to mark manual auditing.

The present invention belongs to the technical field of triboelectric nanogenerators, and particularly relates to a contact-separation mode triboelectric sensor and its preparation method and application. The triboelectric sensor is constructed by using the contact-separation mode, with the Ag interdigital electrode and the PTFE spray coating on the surface of the high-density EVA sponge as the lower friction layer and the multilayer stacked PVA-acrylene fluorescent layer as the upper friction layer. The triboelectric sensor is applied to a triboelectric power generation motion detection system, which can be used to detect the motion characteristics at different joints of the human body. The triboelectric motion detection sensor proposed by the present invention has a faster response speed and better motion intensity resolution. By converting external mechanical stimuli into electrical pulses of corresponding intensity through the triboelectric sensor, it does not require battery power supply, avoiding the dependence of the sensor on battery power.

The invention relates to the technical field of wearable devices, and provides a wearable device control method and device, a medium, a wearable device and a program product, and the wearable device control method comprises the steps that the exercise intensity of a user is acquired, and the user wears the wearable device; acquiring a contact condition between a user and the wearable device; and determining control parameters of the wearable device at least according to the contact condition and the exercise intensity, and controlling the wearable device according to the control parameters. The control parameters are adjusted according to at least the contact condition and the exercise intensity, so that the power consumption of the wearable equipment is always matched with a real use scene, the energy waste is avoided, and the use duration after single charging is remarkably prolonged.

This application discloses a method and apparatus for dynamically adjusting an AR user interface, relating to the field of augmented reality technology. The method includes: extracting features from real-time detected user head motion data to obtain a head motion state; the head motion state includes head motion rate and head motion direction; switching the interface mode of the AR user interface based on the head motion rate, with different interface modes corresponding to different sizes of the AR user interface; and determining the motion of the AR user interface based on the head motion direction and head motion rate. This solves the problem that existing AR user interfaces struggle to simultaneously accommodate external environment observation and AR information reading in highly dynamic task environments. It can adjust the interface mode and motion of the AR user interface according to the intensity and direction of the user's head motion, reducing interface occlusion. It provides a wider field of view during user head movement while simultaneously allowing the retrieval of key information from the AR user interface.

The invention provides a video data processing method and a related device, and the method is applied to electronic equipment, and the method comprises the steps: inputting a target video segment into a pre-trained part feature extraction model frame by frame, and sequentially obtaining a plurality of part features corresponding to each image frame; determining a motion track of each human body part in the target video clip according to the plurality of part features corresponding to each picture frame; determining a target motion intensity change image corresponding to the target video clip according to the motion track; and sending the target motion instruction to the intelligent adult product equipment, so that the intelligent adult product equipment moves according to the target motion intensity change image. Thus, the exercise intensity change of the intelligent adult product equipment can be synchronized with the exercise intensity change of the figure in the video played by the electronic equipment, the function of the adult product equipment is expanded, the intelligence of the electronic equipment to video data processing is improved, and the use experience of a user is enhanced.

The invention relates to an activity amount detection method and device, electronic equipment and a medium. The activity amount detection method comprises the following steps: acquiring multiple pieces of motion information of a user, wherein parameters corresponding to the motion information are different; determining a plurality of exercise intensities according to the exercise information; and determining the current activity amount according to each exercise intensity. The current activity amount of the user is determined according to the motion information of the multiple dimensions, the problems that part of the motion information is inaccurate and the motion intensity of part of the dimensions cannot reflect the motion condition of the user are avoided, and therefore the accuracy of activity amount detection is improved.

The invention discloses a dynamic adjustment method and device for an AR user interface, and relates to the technical field of augmented reality, and the method comprises the steps: carrying out the feature extraction of the head motion data of a user detected in real time, and obtaining a head motion state; the head motion state comprises a head motion rate and a head motion direction; switching the interface modes of the AR user interface based on the head movement rate, wherein the sizes of the AR user interfaces corresponding to different interface modes are different; a motion of the AR user interface is determined based on the head motion direction and the head motion rate. The problem that an existing AR user interface is difficult to consider external environment observation and AR information reading in a high-dynamic task environment is solved. The interface mode and the interface motion of the AR user interface can be adjusted according to the motion intensity and the motion direction of the head of the user, and interface shielding is reduced. A larger environment view is provided in the head movement process of the user, and meanwhile key information can be read from the AR user interface.

A method, apparatus, program product, and electronic device for wildlife monitoring and processing are disclosed, relating to the field of image processing technology. The method includes: acquiring monitoring image frames; determining thermal change intensity based on the monitoring image frames; obtaining motion intensity based on pixel differences between the monitoring image frames; obtaining a fused motion intensity based on the thermal change intensity and the motion intensity; determining the target continuity of the target wildlife in the monitoring image frames based on the target wildlife detection results; triggering image acquisition based on the target continuity and the fused motion intensity to obtain a captured image of the target wildlife; and filtering the captured images of the target wildlife to obtain a target image. This method reduces false triggering caused by factors such as wind blowing vegetation, changes in light, or rain and snow, reduces the generation of redundant images at the source, and improves the efficiency of manual screening.