529 results about "Human behavior" patented technology

The present invention discloses an interactive behavior understanding method for posture reconstruction based on skeleton and image features. The steps are as follows: constructing and preprocessing the data set, extracting skeleton and image features, fusing and reconstructing these features, and conducting experimental evaluation and validation. This method retains the purity of skeleton features for human behavior information extraction and uses image features to retain effective environmental information, complementing the model feature information. Skeleton features are extracted using a graph convolution network, enhancing the relevance of input skeleton point information for accurate feature extraction. Effective image features are quickly and accurately extracted through the Vision Transformer network combined with a multi-head attention mechanism.

The invention discloses a human body behavior recognition method and system based on a space-time fusion network, and relates to the technical field of behavior recognition, and the method comprises the steps: obtaining a human body skeleton sequence, carrying out the joint position coding of the sequence, taking the coded sequence as an input feature, inputting the input feature into the space-time fusion network based on graph convolution and Transform, and outputting a human body behavior recognition result; wherein the space-time fusion network comprises a plurality of groups of graph convolution Transform space fusion modules and multi-scale time fusion modules which are connected in sequence, after input features are input, local structure features and global space features are extracted through the parallel adaptive graph convolution module and Transform module respectively, feature fusion is carried out through the correlation fusion module, and the local structure features and the global space features are fused through the correlation fusion module. And the fused features are input into a multi-scale time fusion module, and are subjected to feature extraction through a multi-scale convolution branch and a Transform branch and then spliced, so that a more accurate human behavior recognition result is output.

The invention belongs to the technical field of intelligent perception and mode recognition, and particularly relates to a human body behavior recognition method based on millimeter wave radar and multi-task learning. The method comprises the steps that millimeter wave radar point cloud data and RGB video streams are synchronously collected, and a five-dimensional point cloud scene is generated through three-dimensional analysis and dynamic target extraction; a hierarchical Point Transform network is constructed to extract radar space-time features, and human body key point features are generated by using visual auxiliary attitude estimation; radar and key point features are dynamically fused through a cross-modal attention mechanism, a multi-task joint optimization strategy is combined, behavior classification serves as a main task, attitude estimation serves as an auxiliary task, and a behavior recognition result is output. According to the method, through multi-task cooperation and cross-modal feature interaction, on the premise of ensuring privacy security, the accuracy and robustness of human behavior recognition in a complex scene are remarkably improved, and efficient and reliable technical support is provided for the fields of intelligent monitoring, human-computer interaction and the like.

The invention discloses a human abnormal behavior monitoring method based on a large-model multi-agent, which is executed by a modular multi-agent system deployed on a back-end server, obtains information through a monitoring camera, and comprises the following steps: obtaining a video stream from the monitoring camera by a sensing agent and extracting human body posture features; analyzing the key frame by a scene understanding agent by using a visual large model, and constructing a time sequence dynamic scene graph; the core reasoning agent evaluates the scene semantic conformity based on the pre-trained large model and performs abnormal preliminary judgment; performing fine-grained classification, interpretation generation and risk assessment on the abnormal behaviors; and the report and action agent generates an alarm and records event data. According to the invention, through multi-agent cooperative work and a large model technology, efficient and accurate monitoring of human abnormal behaviors is realized, and the intelligent level of the monitoring system and the abnormal behavior identification accuracy are improved.

The invention discloses a space-time decoupling human behavior recognition method, device and equipment based on a dynamic semantic guide mask. The method comprises the following steps: extracting human skeleton sequence data from an original video and performing data enhancement; based on a dynamic semantic guide mask mechanism, performing mask operation on the skeleton sequence after data enhancement in two dimensions of space and time; respectively sending the skeleton sequence data after mask operation into a query encoder and a momentum encoder, respectively obtaining spatial representation and time representation, constructing cross-domain contrast loss, and carrying out contrast learning training to obtain a human behavior recognition model; and inputting the to-be-recognized video into the human body behavior recognition model to obtain a prediction result of the human body behavior in the to-be-recognized video. According to the space-time decoupling human body behavior recognition method, device and equipment based on the dynamic semantic guide mask, the human body behavior recognition precision and the environmental adaptability are remarkably improved under the condition that manual labeling is not needed.

The invention relates to the technical field of artificial intelligence, and particularly provides a human behavior recognition system and method based on multi-dimensional and multi-scale feature extraction. The system comprises a human body behavior data acquisition module, a human body behavior data transmission module, a human body behavior data storage module, a human body behavior data preprocessing module, an LGAM simple human body behavior recognition network module, an MPFEFN complex human body behavior recognition network module and a human body behavior information application module. The MPFEFN complex behavior recognition network module comprises an initialization unit, a residual error multi-parallel multi-attention feature extraction unit, a residual error multi-parallel channel enhancement and extraction unit, a feature fusion unit and a complex behavior judgment output unit, and the residual error multi-parallel multi-attention feature extraction unit and the residual error multi-parallel channel enhancement and extraction unit are connected in parallel. The recognition accuracy of the easily-confused behaviors is improved, effective recognition of complex human body behaviors is achieved, and the recognition performance of an existing model is improved.

The invention belongs to the field of cable monitoring and protection, and discloses a human behavior identification method and system for cable external damage prevention based on space-time diagram convolution. The method comprises the following steps: acquiring image data and electrical data through a multispectral camera and a sensor respectively; preprocessing the image data, and constructing a skeleton motion topological graph containing time sequence dynamic association; the improved space-time diagram convolution network is utilized to jointly model the multi-scale global space-time features of the action through multi-layer space-time convolution, and the behavior recognition probability is output; and outputting a fusion behavior recognition probability by using a cross-modal adversarial discriminator, judging a risk level and formulating a hierarchical response strategy. According to the invention, joint determination of behavior identification probability and electrical characteristics is realized, the problem of low damage behavior identification precision under a complex background is solved, and artificial cable external force damage accidents can be effectively prevented.

The invention relates to the technical field of computer vision, in particular to a human body behavior recognition method and system. According to the method, a multi-head space hypergraph convolution module is arranged before each time graph convolution of an ST-GCN model, and a human body behavior recognition model is constructed; the multi-head space hypergraph convolution module constructs a non-uniform hypergraph to represent the topological relation of the human skeleton through the maximum number, which can be contained by hyperedges, of each node, and generates the output of the module in combination with a physical adjacency matrix reflecting the relation of each joint; virtual connection is also added, virtual features which are the same as the input channel dimension and the time dimension of the multi-head space hypergraph convolution module are constructed, and the virtual features and modal data features are spliced along the joint dimension and then serve as module input again, so that global semantic information of real nodes is enriched, generalization information of human body behavior modes is supplemented, and the real-time performance of the multi-head space hypergraph convolution module is improved. And stage dense connection is introduced between different layers to smooth the change degree of the features, so that the human behavior recognition precision is improved.

The invention discloses a human body behavior recognition method and system based on space-time decoupling multi-scale feature fusion. The system comprises a local space-time multi-scale feature fusion network MDCNN and a space-time decoupling network DTST. Extracting different scale features of multichannel sensor data by using a multi-scale convolutional neural network, extracting shallow layer features and deep layer features of signals at the same time, and extracting features representing action information by fully utilizing complementary information among different level features; the method comprises the following steps of: decoupling a network into a time decoupling structure and a space decoupling structure through a time-space decoupling network based on Transform, so as to accurately extract time-space characteristics in an inertial signal; and finally, using a Softmax classifier to carry out action classification and recognition. According to the method, consistency and complementarity among multi-sensor data can be efficiently utilized, multi-scale spatial-temporal characteristics are fused, changes in time dimensions and relevant information in spatial positions are fully considered, different attention degrees are kept for signals of different sensing channels, and the recognition precision in human behavior recognition is improved.

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 discloses an environment situation awareness data engineering system based on multi-source data fusion, which relates to the related technical field of environment situation awareness and comprises a multi-source data acquisition module, a multi-source data processing module, an environment evolution analysis module, an ecological restoration evaluation module, an ecological system evolution module and a visual observation module. The invention further discloses an environment situation awareness method based on multi-source data fusion, which comprises the steps of collecting environment data, effectively integrating the data, and presenting the data to a terminal user in a chart by utilizing a model evolution trend, a prediction result and possible influence of environment change on human behaviors. The UEB model is suitable for various climate and topographic conditions, has high precision and reliability in a high and cold plateau complex environment, can help to analyze and predict the influence of human activities on environmental elements and the influence of environmental changes on human behaviors, deeply excavates the interaction between the human activities and the environmental changes, and improves the prediction accuracy of the human activities and the environmental changes. And insight is provided for decision makers.

The invention provides a human behavior recognition method based on fusion of multi-modal data acquired by an unmanned aerial vehicle, and the method comprises the following steps: splicing a joint Tokens with a space CLS Tokens of the same frame after feature extraction is completed by using a space Transform, and obtaining a feature fusion module; performing structure coding on the joint point sequence based on a human anatomy structure, and constructing a human topological structure association module; the method comprises the following steps of: constructing a time sequence cross Transform module; the space Transform, the feature fusion module, the human body topological structure association module and the time sequence crossing Transform module are stacked to obtain a trunk feature extraction network; a 10-layer trunk feature extraction network is adopted to perform deep feature extraction on input early-stage fusion data and feature fusion data, and classification is performed by using a classification head; the features output by the backbone network are mapped to the dimensions with the same number as the behavior categories, a prediction score is output for each category, and a human body behavior recognition result is obtained according to the prediction scores. According to the method, the human body behavior in the multi-modal data acquired by the unmanned aerial vehicle can be effectively identified.

To provide a behavior analysis device capable of quantifying the relationship between the behavior of a person and other behaviors of the same person, the time of day, the day of the week, the operating status of the device, and the behaviors of others, etc. as a degree of similarity, and a behavior analysis method.SOLUTION: A behavior analysis device 1 includes: an information acquisition unit 11 that acquires information including at least sensor data generated by human behaviors, and converts the acquired information into words; a calculation unit 12 quantifies the similarity, as the relationship between words, among multiple words associated with human behaviors, which are converted into words by the information acquisition unit; and a display unit 14 that displays the similarity among the words calculated by the calculation unit or the time series change of the similarity among the words.SELECTED DRAWING: Figure 1

The invention discloses a deep learning-based human behavior recognition method and recognition system, and the method is characterized in that the method comprises the following steps: S1, data collection: collecting video data containing a plurality of human behaviors through a high-definition camera or a video database, and covering different scenes, illumination conditions and human postures; s2, data preprocessing: firstly extracting a three-dimensional coordinate sequence of human skeleton key points by using a Medape pre-training model, and then performing mean filtering and normalization preprocessing on the collected data to improve the generalization ability of the model; according to the method, the advantages of the DCN and the LSTM are combined, the improved double-flow convolution model is added with the space-time soft attention mechanism, the problems that space-time feature extraction is insufficient and feature design is complex in human body action recognition are effectively solved, the lightweight degree of the model is remarkably improved, and the human body action recognition accuracy is improved. And a new method is provided for research and application of human behavior recognition.

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 present technology is directed towards methods to model human-human cooperative behaviors in joint actions for applications in eventual human-robot interactions. A computer-implemented bounded rationality game-theoretical model is utilized to model human behavior.

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 relates to a behavior recognition method and system based on adaptive sliding window multi-head self-attention, and the method comprises the steps: preprocessing a training set of a human behavior video data set, inputting the preprocessed training set into a neural network model for training, preprocessing a test set, and inputting the preprocessed test set into the trained neural network model for behavior detection, obtaining the detection accuracy of the neural network model; and inputting to-be-recognized human body behavior video data into the tested neural network model for behavior recognition. According to the method, the long-time dependency and global features in the video can be captured, so that the extracted features are more comprehensive, the problems that the receptive field of the convolutional neural network is limited and the long-time dependency relationship in the video is difficult to capture can be effectively solved, and the recognition accuracy of various behaviors is greatly improved.

This invention presents a computer-implemented method for answering polls and surveys using large language models (LLMs), a novel approach that leverages an LLM's ability to emulate a group of human responses for diverse data collection. The system involves training a single or multi-modal LLM on a comprehensive dataset comprising one or more categories of text, image, sound and other sensory input data, which can be continuously updated with current events and trends, to ensure accurate representation of human behaviors and opinions. Utilizing a user interface, the method includes receiving survey and poll questions, posing these questions to a unique instance of the trained LLM and receiving the answers, recording the question-answer sessions, and compiling the results for presentation. Additionally, the system prioritizes data privacy and integrates a feedback mechanism for continuous improvement, providing an efficient solution to answering surveys or polls in the digital age.

To correctly estimate behavioral factors of object person's behavior.SOLUTION: A behavioral factor estimation device 100 uses a behavioral factor estimation model to estimate behavioral factors of object person's behavior. The behavioral factor estimation device 100 determines whether learning data of behavior and work situation corresponding to newly estimated object person's behavior and work situation is included in learning data used in previous learning by the model, for example, by referring to estimation feasibility determination setting H. The behavioral factor estimation device 100 determines whether it is possible to estimate the behavioral factors of the object person's behavior using the model based on determination results. The determination results can include, for example, three situations such that: (1) estimation is possible without re-learning by the model; (2) re-learning by the model may be effective; and (3) risk of erroneous determination is high.SELECTED DRAWING: Figure 1

The invention provides a human body behavior recognition method and system based on key frame extraction, and the method comprises the steps: obtaining the three-dimensional position information and three-dimensional angle information of each joint point, and constructing a position information set and an angle information set of each frame of image; constructing a multi-dimensional data set of each frame of image; dividing images in the target monitoring video into K initial clusters based on a frame sequence, defining a left boundary set and a right boundary set, and iteratively traversing the left boundary set and the right boundary set based on an improved KKF algorithm to obtain K final clusters; and obtaining a feature mean set of each final cluster, calculating a feature distance, screening out a minimum feature distance from a plurality of feature distances under the same final cluster, screening out a key frame image under each final cluster according to the minimum feature distance, and constructing a human behavior recognition model according to the key frame images. According to the invention, the accuracy of human behavior recognition can be 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 present disclosure relates to the field of artificial intelligence, and in particular to a large language model training method for the urban field and a city generative intelligent method and device. The training method includes: obtaining a general large language model and an initial data set; obtaining a composite data set by setting an intelligent agent in a virtual urban scene to simulate various behaviors of humans in a real urban scene; mixing the composite data set with the initial data set into a pre-training data set, and using the pre-training data set to incrementally pre-train the general large language model to obtain a first urban large model; using a fine-tuning data set constructed for the urban field to fine-tune the first urban large model to obtain a second urban large model; using a human preference data set to perform preference alignment training on the second urban large model to obtain a target urban large model. In this way, a target urban large model with professional knowledge in the urban field, universal common sense in the world, and cognitive reasoning capabilities can be obtained.

The invention discloses an output matching method based on a smart humanization engine, and the method comprises the following steps: S1, defining personality features, and building the association configuration of the personality features and online platform user behaviors on an online platform; s2, acquiring user behavior data on the online platform in real time; s3, according to the association configuration established in the step S1, converting the user behavior data into a personality feature score of the user, and establishing a user portrait big data model according to the personality feature score; s4, for any user, on the basis of the user portrait big data model, carrying out AI model construction to form a simulated digital person based on a smart humanization engine, simulating behaviors of the simulated digital person, and collecting behavior data of the simulated digital person in different virtual environments; and S5, according to the behavior data, collected in the step S4, of the simulated digital human in different virtual environments, predicting the behavior trend and demand of the corresponding user in real life.

The invention discloses an electric power production human behavior identification method, device and equipment based on a skeleton, relates to the technical field of electric power safety, and can solve the technical problems of low efficiency and high labor cost caused by the fact that whether human behaviors in electric power production are standard or not needs to be manually monitored at present. Comprising the steps that a to-be-recognized power production human body behavior video is acquired, skeleton sequence data are generated according to the power production human body behavior video, and the skeleton sequence data comprise at least one group of actions; the skeleton sequence data is input into a trained power production human behavior recognition model for recognition, a target power production human behavior is obtained, the power production human behavior recognition model comprises a space-time diagram convolutional network, a space-time transformer network and a recognition module, the skeleton sequence data is input into the space-time diagram convolutional network, a first feature is obtained, and a second feature is obtained; and inputting the skeleton sequence data into a space-time transformer network to obtain a second feature, and inputting the first feature and the second feature into a recognition module to obtain a target power production human behavior.

The invention discloses a sound mixing and noise reduction method and system, and the method comprises the steps: firstly, carrying out the recognition of the emotional state of a person in a vehicle through an emotion recognition method; and then, whether the person in the vehicle has a bad emotion which can influence the behavior state or not can be judged according to the emotion recognition result. And if the person in the vehicle does not have the impatient emotion, the current noise in the vehicle does not need to be processed. On the contrary, if the person in the vehicle has a restless emotion, the sound signal in the vehicle is immediately collected through the sound pick-up. And finally, a sound wave identification method can be adopted to extract a noise feature vector of a noise sound segment which affects the emotion of people in the vehicle from the collected sound signals, and a corresponding sound mixing sound wave band is generated based on the extracted noise feature vector and is fused with the noise sound segment which affects the people in the vehicle, so that the sound mixing sound wave band becomes a sound segment which can be accepted by people. Therefore, the influence of noise on the behavior state of people is reduced.

A device performs operations including determining a probability that a vulnerable road user (VRU) will continue on a current path (e.g., in connection with controlling an autonomous vehicle). The device receives an image depicting a vulnerable road user (VRU). The device inputs at least a portion of the image into a model, and receives, as output from the model, a plurality of probabilities describing the VRU, each of the probabilities corresponding to a probability that the VRU is in a given state. The device determines, based on at least some of the plurality of probabilities, a probability that the VRU will exhibit a behavior, and outputs the probability that the VRU will exhibit the behavior to a control system.

The invention discloses a multi-human-body-target behavior recognition method based on millimeter-wave radar. The method comprises the following steps: firstly, acquiring human body behavior data by deploying a millimeter wave radar, and obtaining a multi-dimensional parameter signal of human body behaviors by adopting 4D-Fast Fourier Transform (4D-FFT); secondly, a high-dimensional matrix signal is converted into a point cloud matrix through a cell average constant false alarm rate (CAFAR) detection algorithm (CA-CFAR) and geometric space coordinate conversion, so that the complexity of a radar signal is effectively reduced; secondly, further compressing the point cloud matrix through a density-based spatial clustering (DBSCAN) algorithm and a local outlier factor (LOF) algorithm based on space and speed information to remove dynamic interference and static interference in the environment, and then realizing segmentation of the multi-target point cloud matrix through a Hungary algorithm to obtain a pure single-target point cloud matrix; and finally, realizing accurate recognition of human body behaviors through a neural network based on the multi-dimensional radar point cloud. According to the invention, through radar point cloud and deep learning, the complexity of radar signals is reduced and accurate recognition of human body behaviors is realized at the same time.

A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.

The invention relates to the technical field of human body behavior recognition, in particular to a human body behavior recognition device and algorithm based on millimeter wave radar and ultrasound, and the method comprises the steps of control management, data acquisition, data processing, data analysis, data storage and command output. Gesture actions of a user are obtained in an ultrasonic and millimeter wave mode, leakage of privacy of the user is reduced, more data can be conveniently obtained through different data obtaining modes, the privacy of the user is protected, meanwhile, the analysis accuracy is guaranteed, when a non-input person is detected, a camera shooting mode is adopted, and the user experience is improved. According to the method, non-input personnel images are continuously obtained, the images can be conveniently used as evidences, and the data acquisition accuracy is further improved by gathering millimeter waves and cleaning air at local positions.