154 results about "Human behavior" patented technology

The invention relates to the technical field of safety monitoring, and discloses a multi-source data fusion-based personnel behavior safety early warning system, which comprises a data acquisition module, a fusion decision module, a dynamic adjustment module, a cross validation and correction module, an accumulated score management module, an early warning comparison module, an early warning response module and an equipment linkage module, the data acquisition module acquires identity permission, dynamic position, environmental perception and video stream data in real time, and encrypts and transmits the data to the fusion decision module, the fusion decision module performs modeling by using an improved D-S evidence theory, identifies illegal behaviors and gives initial scores, the dynamic adjustment module performs weighting according to time and position coefficients to obtain dynamic scores, and the dynamic adjustment module performs decision making according to the dynamic scores. The cross validation module calculates correction scores such as conflict coefficients, the score accumulation module performs rolling accumulation according to 24 hours, attenuation and reset rules exist, the early warning comparison module marks three-level threshold values to determine risk levels, the early warning response module triggers corresponding strategies according to the levels, and the equipment linkage module controls hardware to realize closed-loop control so as to guarantee regional safety.

The invention provides a through-the-wall radar human body behavior recognition method and recognition system based on spatial-temporal characteristics, and the method comprises the steps: obtaining and processing through-the-wall radar human body behavior echo sampling signals, and obtaining a corresponding time-Doppler spectrogram as a sample set; and training a constructed TWRMama network by using the sample set, in which the network adopts a CA-Mama block as a core feature extraction module, can efficiently model and input a time sequence dynamic feature and a spatial dependency relationship of a time Doppler spectrogram, and effectively enhance the feature expression ability in a shielding scene, thereby realizing accurate recognition of human behaviors of the through-the-wall radar. Besides, information interaction between patches is introduced into a patch embedding module of the TWRMama network, so that the expression capability of the network can be further improved, and the calculation complexity of the network is effectively reduced.

The invention discloses a human body behavior prediction method and system, and the method comprises the steps: carrying out the skeleton point sequence extraction of a real-time behavior video image of a target person, and obtaining a joint point coordinate set and a skeleton motion sequence; determining a spatio-temporal feature sequence of the joint point coordinate set, and determining a skeleton motion sequence to perform spatio-temporal attention coding to obtain global spatio-temporal dynamic features; fusing the action probability distributions corresponding to the spatial-temporal feature sequence and the global spatial-temporal dynamic features to obtain short-time action probability distribution data; candidate action screening is carried out on the short-time action probability distribution data, and candidate action comprehensive features are obtained; performing feature coding on the historical action sequence of the target person to obtain a historical context vector, and splicing the historical context vector with the candidate action comprehensive features to obtain a fusion feature; and inputting the fusion features into a probability model for intention probability evaluation to obtain a behavior prediction result of the target person. According to the method, the accuracy of behavior prediction is improved.

The invention provides a radar-based human skeleton estimation method, system and product, and the method comprises the steps: carrying out the target detection of collected radar echo data, and generating a target four-dimensional point cloud containing distance information, speed information and angle information based on a target detection result; dividing the target four-dimensional point cloud into a plurality of human body topology point cloud blocks based on the velocity direction similarity of each point in the target four-dimensional point cloud; and inputting the human body topology point cloud block into a human body skeleton estimation network for human body skeleton estimation to obtain a human body skeleton estimation result. According to the human skeleton estimation method provided by the invention, human topology priori can be constructed from sparse millimeter wave radar point clouds, point cloud block features sensed by a structure are extracted, the modeling capability of a model for the spatial relationship of key parts of a human body is enhanced, diversified and natural daily human behaviors in a non-inductive monitoring scene can be adapted, and the human skeleton estimation accuracy is improved. The human body skeleton estimation with higher generalization ability is realized, and the accuracy and robustness of human body posture prediction can be effectively improved.

The application discloses a kind of spatio-temporal decoupling human behavior recognition method, device and equipment based on dynamic semantic guide mask, comprising: extracting human skeleton sequence data from original video and carrying out data enhancement;Based on dynamic semantic guide mask mechanism, the skeleton sequence after data enhancement is masked in operation in two dimensions of space and time;Skeleton sequence data after masking operation is sent into query encoder and momentum encoder respectively, spatial representation and time representation are obtained respectively, cross-domain contrast loss is constructed, contrast learning training is carried out to obtain human behavior recognition model;The video to be identified is input into human behavior recognition model, and the prediction result of human behavior in the video to be identified is obtained.The spatio-temporal decoupling human behavior recognition method, device and equipment based on dynamic semantic guide mask of the application significantly improve human behavior recognition precision and environmental adaptability without manual label.

The invention relates to the technical field of people flow simulation analysis, and particularly provides a people flow simulation analysis method and system based on spatio-temporal behavior dynamics, and the method comprises the steps: obtaining urban multi-dimensional spatio-temporal heterogeneous data, and carrying out the standardization; constructing a dynamic coupling mechanism of crowd behaviors, spatial constraints and industrial function supply, and extracting travel decision rules and destination selection logic; by taking the mechanism as a constraint, constructing a space-time behavior dynamic model in combination with subject modeling, simulating group flow space-time evolution and outputting crowd flow characteristic parameters; and based on the parameters, predicting the people flow distribution and aggregation dissipation trend of the target scene, and performing visual display. According to the method, through combination of multi-factor dynamic coupling and subject modeling, simulation accuracy and scene adaptability are improved, a prediction result is visual, and scientific decision support can be provided for urban planning, traffic management, public safety guarantee and the like.

A methodology for auditing the visual diversity of unlabeled human face image datasets uses a set of core human interpretable dimensions derived from human similarity judgments. Given a face image, a model can output dimensional values aligned with the human mental representational space of faces, where values not only express the presence of a feature, but also its extent. Since the model can be learned entirely from human behavior, the learned dimensions are not biased toward features that are easier to verbalize or quantify.

The present application relates to the technical fields of computer vision and human behavior recognition, and discloses a kind of industrial standard operation step generation method and system based on human behavior sequence recognition;The present application, first, the continuous video of operation area is acquired, and video frame is time-synchronized and spatially aligned;Determine local image area with wrist position, identify the contact relationship and position change between hand, tool and workpiece;According to the continuous change of action state, action segment is divided, and invalid action is filtered out and continuous repeated action is combined according to workpiece state and process sequence before and after action;According to the time sequence of reserved action segment, tool category, action type and workpiece position are determined, and industrial standard operation step is generated;Thereby, subtle action misrecognition, step omission, repetition and sequence error are reduced, and the accuracy of standard operation step generation is improved.

This behavior analysis device (1) is provided with: an information acquisition unit (11) that acquires information including at least sensor data generated in association with a person's behavior, and words the acquired information; a calculation unit (12) for numeralizing the similarity as the relationship between words for the plurality of words associated with the human behavior obtained by the information acquisition unit as a word-to-word relationship; and a display unit (14) that displays the inter-word similarity or the temporal change in the inter-word similarity calculated by the calculation unit. Provided are a behavior analysis device and a behavior analysis method for numeralizing the relationship between the behavior of a person and other behaviors of the same person, the time, the week, the operating state of the device, the behavior of another person, and the like as similarity.

The application discloses a human behavior recognition method based on a space and time sequence dual-channel fusion model, constructs a deep learning network architecture fusing an ER3D model and a Space-TimeTransformer model, and obtains an initialized network through end-to-end pretraining on a public human behavior recognition dataset, then reads a human behavior training video, carries out fixed interval sampling and data enhancement on the training video, feeds the video frames after data enhancement into the pre-trained network architecture for training, and generates a human behavior recognition model.In the test stage, the human behavior video in the test set is read, sampled and cut, and then fed into the trained recognition model for recognition, and the whole process realizes end-to-end detection of human behaviors in the video.According to the characteristics that the ER3D model and the Space-TimeTransformer model can effectively model the space information and the time sequence information contained in the video frames, a deep learning network architecture capable of accurately recognizing human behaviors is constructed and trained.

A method and device for human behavior recognition in ecological protection zones based on gait feature recognition are proposed. The method involves laying a ring-shaped first optical fiber detection layer around the perimeter of the protected area's isolation zone; deploying a radial second optical fiber detection layer along the isolation zone towards the center of the protected area; and deploying a tri-band WiFi probe array within the isolation zone. An intrusion location recognition module uses vibration signals detected by the first optical fiber detection layer to calculate the intrusion location coordinates using a time difference positioning formula, and activates the monitoring channels of the corresponding radial second optical fiber detection layer based on the location coordinates. A gait feature fusion and extraction module extracts multi-scale energy features and generates comprehensive gait features. An identity discrimination module inputs the comprehensive gait features into a pre-trained classification model and determines the identity of the intruder based on the comprehensive gait features, thereby constructing an intelligent, dynamic, interconnected, and low-cost ecological protection technology system for ecological protection zones.

The invention provides a multi-personality decision simulation and game method for military artificial intelligence. Simulation of an AI opponent model on complex human behaviors is enhanced through a DIKWP semantic framework. The system firstly configures personality parameters based on a psychological model (such as MBTI, big five personalities), and maps the personality parameters to an AI cognitive model to construct a behavior preference model; then, a noise injection algorithm is adopted to simulate emotion and cognitive deviation, and unique deviation of different personalities in decision making is reproduced; the multi-agent self-gaming engine enables AIs with different personality characteristics to repeatedly game in a red-blue confrontation environment, optimizes a strategy and evaluates winning rate distribution. And the decision fusion analysis module summarizes the data, generates personality performance comparison and behavior pedigree diagrams under each tactical situation, and predicts opponent behaviors. The DIKWP semantic integration interface ensures that a personality simulation result can be seamlessly connected with a knowledge graph and a strategic intention module of an existing AI system. According to the method, AI opponent modeling is enriched on the semantic level, the decision simulation degree of a real commander is improved, and as a supplement of an existing AI simulation system, the ability of military AI in the aspects of strategic pre-judgment and cognitive game is enhanced.

Various methods and processes, apparatuses or systems, and media for classifying human behaviors in various domains by using ensemble learning to perform sequence modeling with respect to sequence data are disclosed. The method includes: receiving a first set of data; partitioning the first set of data into a set of respective data streams, each respective data stream corresponding to a respective agent; extracting, from a first data stream, a first sequence of observations that relates to a first agent; inputting the first sequence of observations to each of several models that are trained by using historical data relating to the first agent; using the models to generate a composite score that relates to the first sequence of observations; and determining, based on the composite score, whether the first sequence of observations indicates at least one anomaly that relates to a behavior of the first agent.

The application discloses a video human behavior prediction method based on residual diffusion theory and skeleton points, comprising the following steps: S10, acquiring an original group motion sequence, decomposing the multi-person sequence into individual trajectories, and then converting the time-domain motion sequence of each individual into frequency coefficients through discrete cosine transform (DCT); S20, based on the frequency domain representation, introducing a residual term to construct a forward diffusion process; S30, defining a weighted residual noise as a learning target, and realizing conversion from noise to residual noise; S40, determining an optimal step through transformation, realizing acceleration and unified training and reasoning; S50, applying physical constraints to single-person prediction, introducing a weighted combined biomechanical regularization loss function to constrain the physical feasibility of the generated sequence; and S60, synthesizing a group sequence and outputting a predicted sequence. The application utilizes deep learning technology, extracts human skeleton point information, combines a residual diffusion mechanism, predicts human behavior in a video sequence or a real-time scene, and realizes high-precision prediction of future behavior.

The application relates to a human body behavior recognition method and system based on visual semantic text enhancement, and the method comprises the following steps: S10, acquiring human body behavior WIFI sensing data and human body behavior video frame data; S20, respectively extracting WIFI amplitude energy images and high-dimensional semantic feature vectors from the human body behavior WIFI sensing data and the human body behavior video frame data; S30, performing deep correlation fusion on the WIFI amplitude energy images and the high-dimensional semantic feature vectors, and calculating the probability distribution of human body action categories. The human body behavior recognition method based on visual semantic text enhancement provided by the application realizes effective fusion of semantic information and WiFi features by cross-modal similarity retrieval, and calculates the probability distribution of human body action categories based on the global feature vector obtained after fusion, so that the recognition precision of similar actions is effectively improved.

The invention belongs to the technical field of computers, and provides a Kolmogorov-Arnold convolution filter-based transfer learning method for human behavior recognition, and the method comprises the steps: firstly collecting and preprocessing sensor data of a wearable device, dividing the sensor data into a training set, a verification set and a test set, constructing a recognition model, and carrying out the recognition of the sensor data through a Kolmogorov-Arnold convolution filter. The method comprises the following steps: accessing a prompt module based on Kolmogorov-Arnold convolution to an input end of a pre-trained time sequence basic model with frozen backbone network parameters, accessing a classifier to an output end of the pre-trained time sequence basic model, then finely adjusting the prompt module and the classifier only by using training data, determining an optimal model through a verification set, and finally obtaining a time sequence basic model; and finally, the trained model is deployed on wearable equipment, real-time and high-precision human body behavior recognition is realized, and the conversion cost between different recognition tasks is greatly reduced.

The invention discloses an omni-directional multi-degree-of-freedom robot motion structure simulating human behaviors and actions. The motion structure comprises an upper limb trunk supporting platform, a waist connecting mechanism and a lower limb connecting plate. The three groups of waist connecting structures are circumferentially and uniformly distributed around the symmetric center of the lower limb connecting plate and are mounted on the lower limb connecting plate, one group of waist connecting structure comprises a driving motor, the driving motor is mounted on the lower limb connecting plate, and two ends of a driving shaft are connected with two output shafts perpendicular to the driving shaft; the output shaft is connected with a mounting seat mounted at the bottom of the upper limb trunk supporting platform through a fisheye joint shaft, and the fisheye joint shaft is movably connected with the output shaft and the mounting seat. The motion structure is reasonable in structural design, effective transmission of power is ensured, and the robot can achieve more complex actions similar to the waist of a person; real-time PID control is carried out on the three driving motors based on an inverse kinematics model, accurate control over pitching and side swaying of the waist can be achieved, and the waist can be controlled to twist to a certain degree.

The invention discloses a video human body behavior prediction method based on a residual diffusion theory and skeleton points, and the method comprises the steps: S10, obtaining an original group motion sequence, decomposing a multi-person sequence into individual trajectories, and converting the time domain motion sequence of each individual into a frequency coefficient through DCT (Discrete Cosine Transform); s20, based on frequency domain representation, introducing a residual term to construct a forward diffusion process; s30, defining weighted residual noise as a learning target, and realizing conversion from noise to residual noise; s40, determining an optimal step through transformation, and realizing acceleration and unified training reasoning; s50, applying a physical constraint to single-person prediction, and constraining the physical feasibility of a generated sequence by introducing a weighted combined biomechanical regularization loss function; and S60, synthesizing a population sequence, and outputting a prediction sequence. According to the method, the behavior of the human body in a video sequence or a real-time scene is predicted by extracting human body skeleton point information and combining a residual diffusion mechanism by using a deep learning technology, and high-precision pre-judgment of future behaviors is realized.

This invention provides an information processing device, an information processing method, and an information processing program that can provide a diverse virtual space participation-based mobility environment that takes into account the prediction of human behavior in relationships with others, i.e., interactions. [Solution] The information processing device comprises a measurement unit that measures the movement simulations of multiple users in real space and reflects the measured movement simulation results in a virtual space; a storage unit that stores the movement status of multiple users; a model generation unit that generates simulation model data based on the movement status and stored data; and a simulation execution unit that executes a simulation based on the model data reflected in the virtual space when multiple users participate in the virtual space using different means of transportation.

The application discloses a method and system for identifying abnormal behavior based on multi-modal information fusion, and belongs to the technical field of information fusion. The method comprises the following steps: acquiring the joint position information of a target person in a target scene; prompting the identification model to determine the human behavior characteristics of the to-be-identified person based on the space-time change information of the joints of the to-be-identified person; prompting the database to determine the human vital signs of the to-be-identified person based on the human vital sign information of the to-be-identified person; performing multi-modal information fusion on the human behavior characteristics and the human vital signs of the to-be-identified person to generate a data set for determining the state of the to-be-identified person, and determining whether the state of the to-be-identified person is abnormal based on the data set. The application can identify the abnormal behavior of the person in the target scene, can identify the behavior of the caregiver, can determine the behavior state of the caregiver in time, and can timely issue an alarm for the abnormal behavior state of the caregiver.

The invention discloses a strip mine personnel falling detection method based on a Markov random field, and belongs to the technical field of human body behavior recognition and safety monitoring. In order to solve the problem of high false alarm rate in a mine vibration environment depending on an accelerometer in the prior art, the invention provides a method which takes a gyroscope as a core sensor, extracts two time sequence characteristics of impact strength and turnover degree representing a body motion state, and constructs an action state transition model based on a Markov random field for analysis. According to the method, the time sequence rule of continuous actions of normal, unbalanced, falling and falling can be effectively learned, and the falling probability can be accurately calculated. When the probability exceeds a set threshold value, the system automatically triggers an alarm. According to the method, the detection specificity is remarkably improved from the sensing principle and algorithm model level, experiments show that the detection accuracy rate of the method in the complex scene of the strip mine exceeds 98%, the false alarm rate is reduced by 60% or above, and the safety of operators is effectively guaranteed.

The present application relates to the technical field of human behavior recognition, and particularly relates to a human behavior recognition method of a dual-channel mixed graph convolution network, comprising data preprocessing; a graph convolution module construction based on feature similarity; a graph convolution module construction based on individual characteristic difference; adding a dual-channel fusion based on self-attention to construct a human behavior recognition model of a dual-channel mixed graph convolution network based on feature similarity and individual characteristics. The present application is complementary to each other through the similarity between action features and the difference between individual actions, and can be adaptively fused to obtain more deeply related information for the classification task.

The application provides a human behavior recognition method and system based on a multi-channel directed graph convolution, comprising: obtaining a human skeleton data sequence to be recognized, and performing corresponding preprocessing to obtain joint flow, skeleton flow and motion flow data; performing directed graph construction based on the preprocessed skeleton data to obtain a correlation matrix corresponding to the human skeleton data; inputting the joint flow, the skeleton flow and the motion flow into a pre-trained multi-channel directed graph convolution model respectively, using pre-set classifiers to obtain scores of different categories corresponding to different flows based on feature information output by the multi-channel directed graph convolution model, and adding the scores of the categories corresponding to different flows to take the category with the maximum score as a final human behavior recognition result.

To estimate human behavior with high accuracy. [Solution] The information processing device 10 comprises a posture estimation unit 15C and a behavior estimation unit 15D. The posture estimation unit 15C estimates the posture of person P included in the video. The behavior estimation unit 15D estimates the behavior of person P based on the position of person P, the posture, and the duration t of the posture.

This invention discloses a fall detection system and recognition method based on radar echo noise reduction, belonging to the field of human behavior perception technology. The system includes the following steps: In a fall detection and recognition scenario, information on changes in human distance, human movement speed, and human posture collected by millimeter-wave radar is synchronously processed and spliced ​​in chronological order to form a time-series of height changes reflecting the gradual decrease in human height. This invention, by constructing a time-series of continuous evolution of human height and introducing a set of slow-falling behaviors and spatial boundary offset features, effectively identifies the process of slowly falling over furniture. Furthermore, by combining phased judgment, time backtracking, and observation rhythm adjustment mechanisms, it continuously observes and dynamically judges high-risk fall behaviors without relying on violent movement characteristics, improving monitoring reliability and timely alarm.

The application discloses a kind of dimension self-adapting human behavior prediction method and equipment, the method first acquires the D-dimensional time series containing human behavior, carries out modal decomposition processing, obtains each dimension mode.Secondly, for each mode, the internal relationship between the implied state, activity contribution and observation event is constructed based on the dynamics equation.Then estimate each parameter of the dynamics equation, and construct total parameter set.Finally, the prediction sequence of each mode is inferred based on the total parameter set, and is reconstructed as D-dimensional time series, realize the prediction of human behavior.In the equipment, the central processing module processes the human behavior data collected by the perception module, the abnormal behavior control interaction module alarms, the communication module is used to send the abnormal data back to the personal computer end, and the data is transmitted to the storage module for saving when power failure or network interruption.The application does not need prior knowledge when carrying out human behavior prediction, low cost and high accuracy.

The application belongs to the technical field of non-contact human behavior, and discloses a multi-modal layered cross fusion recognition method based on vision and WiFi. The method uses common commercial cameras and WiFi signal acquisition equipment to sample human behavior, cooperates with a special deep learning network to extract discriminative features of videos and WiFi signals, uses a multi-modal layered cross fusion method to mine complementary information relationship of the two modalities, realizes complementation of heterogeneous information, eliminates redundancy between information, makes up for the problem of inaccurate recognition of single modal in adverse conditions (obstacles, dim light, improper angle, etc.), and realizes high-precision and high-robustness recognition of human actions. The application fills the vacancy of multi-modal fusion and complementation in the field of non-contact sensing, and creates an application example for passive sensing and recognition based on deep learning. The method can be widely applied to fields such as precise care, diet monitoring and intrusion detection.

The invention discloses a behavior identification and abnormity early warning method, device and system applied to a terrace classroom. The method comprises the following steps: acquiring audio data and video data; positioning the audio data, and performing association matching on the audio data and people in the video data to obtain video modal spatial-temporal features and audio modal embedding features; inputting the video modal spatial-temporal features and the audio modal embedded features into a cross-modal behavior recognition module to recognize behaviors of all people in the video data to obtain behavior data of all people in the video; inputting behavior data of all people in the recognition video into an anomaly detection channel to recognize abnormal behaviors of people; and outputting a behavior abnormal result prompt according to the behavior data and the causal knowledge graph in response to the fact that the output result of the abnormal detection channel is an abnormal behavior. The problem of identity-behavior correspondence caused by serious shielding and variable view angles in a large scene can be solved, and the accuracy of abnormal behavior recognition is effectively improved.

The application belongs to the technical field of behavior recognition, and particularly relates to a human behavior recognition method and system based on dynamic space-time modeling and semantic quantification, which comprises the following steps: acquiring human skeleton sequence data and converting the data into space-time feature representation; constructing a Transformer network based on dynamic space-time modeling and semantic quantification, which comprises a preprocessing layer, a progressive feature extraction layer and a classifier; the preprocessing layer generates initial space-time features; in the progressive feature extraction layer, the joint importance weight is adaptively assigned by a global dynamic joint weighting module in the shallow stage, and the global dynamic joint weighting and a semantic quantification module are simultaneously used in the deep stage, the continuous features are discretized into semantic indexes by using a learnable motion primitive codebook, a semantic graph is constructed based on hard assignment and is fused with a physical skeleton graph, and the differentiable feature reconstruction is performed based on soft assignment; finally, the semantic enhanced features are input into the classifier to complete behavior recognition. The application improves the precision, interpretability and generalization ability of behavior recognition.

The invention discloses an action recognition method based on a double-geometry guidance time-frequency decoupling graph convolutional network, relates to the technical field of human body behavior recognition, and aims to jointly enhance the spatial geometry and time dynamic representation capability of human body actions and overcome the limitation of the prior art. The core of the method comprises a double-geometry refining attitude guidance module and a time-frequency decoupling dynamic coding module, the double-geometry refining attitude guidance module innovatively fuses Euclidean space diagram convolution and Riemannian manifold diagram convolution, and the modeling ability and analysis efficiency of the model for a high-order cooperative relationship in complex skeleton geometry are significantly improved. Therefore, the spatial dependency can be understood more deeply; the time-frequency decoupling dynamic coding module ingeniously fuses capture of time-domain transient response and extraction of a frequency-domain rhythm mode, especially through introduction of an innovative rhythm-KAN module, complementary modeling of short-time dynamic and long-time rhythm is achieved, and the ability of depicting diversified human body dynamic is comprehensively enhanced.