11 results about "Motion intensity" patented technology

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 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 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.

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.

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.

The application relates to the technical field of video processing of computer vision, and provides a smooth round positioning method in a sports video based on group motion information. The method comprises the following steps: obtaining a motion change index of a current video, taking the motion change index as group motion information, and measuring the motion intensity of athletes in the sports video; and positioning the smooth round in the video according to the motion change index, so as to achieve the purpose of segmenting the sports video. In the application, the tracking result of the athletes in the video is expressed in a matrix form, the smooth round and the non-smooth round are expressed as features which are easy to distinguish, and the positioning method of the smooth round is adopted, so that the ideal positioning effect of segmenting the sports video is achieved.

The application relates to a skeleton action recognition method based on region perception motion contrast learning, and relates to the field of computer vision. The method solves the problems that existing contrast learning skeleton action recognition methods focus on global feature contrast learning, ignore the importance of local action patterns for semantic discrimination, and a large amount of redundancy exists in the time and space dimensions of a skeleton sequence, and the method comprises the following steps: skeleton data preprocessing and standardization; multi-stream data conversion, generating three kinds of representations of joint flow, motion flow and skeleton flow; motion perception time sequence enhancement, adaptively retaining key frames based on interframe motion intensity; spatial multi-level data enhancement, implementing progressive three-level data enhancement operation; spatiotemporal feature extraction and region perception mining, extracting features through an encoder and unsupervisedly identifying significant motion regions by using a contrast motion perception region mining method; contrast learning optimization, constructing a dynamic negative sample queue to learn features; and multi-stream model fusion, weightedly integrating recognition results of the data streams.

The present application relates to the technical field of sports health, and relates to a control method and a control system of a portable detection assembly based on multiple sensors. The present application sets multiple training modes, and the user selects the training mode and sets the sports item in advance. Thus, the motion state of the user can be recognized more quickly and accurately. Thus, the motion information of the user can be accurately acquired. Meanwhile, the current motion state can be accurately provided to the user through accurate calculation of the motion intensity. In the motion process, the motion period and the motion height information can be accurately determined through cooperation of the motion sensor and the barometer. The motion consumption can be accurately calculated through division of the motion according to the maximum heart rate of the user based on detection of the heart rate, so as to provide suggestions for body control. The body data of a certain number of users is uploaded to the server, and the information is analyzed, so that reasonable suggestions can be better provided.

The application relates to an AI recognition movement detection and video recording control method, and relates to the technical field of artificial intelligence, and comprises the following steps: step one, photon event collection and interframe difference sequence generation: a photon counting sensor array is used to continuously collect frame images of a monitoring scene, and interframe difference images of multiple continuous frames are arranged in time sequence to form an interframe difference sequence; step two, variation mode decomposition is used to separate light components and motion components; step three, a Poisson pulse neural network is used for processing to generate a motion transient intensity graph; and step four, block-level connected aggregation judgment and video recording control: the motion transient intensity graph obtained in step three is divided into image blocks, and block energy values are calculated, and when global motion intensity in a sliding detection window continuously falls below a preset static threshold, a video recording device is controlled to stop recording; the application realizes high accuracy and low false trigger rate of movement detection under complex light scenes such as evening dim light and night vision switching.

The application discloses a multi-dimensional motion state evaluation method and system based on voice biomarker assistance, relates to the technical field of data processing, and comprises the following steps: synchronously collecting voice and heart rate signals during user motion, extracting time domain breathing features and frequency domain stability features as voice biomarkers by separating human voice and silent sections and identifying non-grammatical pauses, simultaneously extracting nonlinear heart rate variability features and recovery indicators as heart rate features after denoising the heart rate signals, inputting a multi-modal fusion model for double-path feature extraction and multi-head attention fusion, and outputting multi-dimensional motion state evaluation results and generating motion intensity adjustment instructions. The application solves the technical problems of the prior art, such as lack of deep heterogeneous fusion of voice and heart rate signals, difficulty in extracting stable physiological features under motion interference, and resulting in one-sided and lagged motion state evaluation results, and achieves the technical effects of improving the comprehensiveness, real-time performance, anti-interference performance and personalized guidance accuracy of motion state evaluation.