17780results about "Biometric pattern recognition" patented technology

Systems and methods are provided for machine learning based monitoring. Image data from a camera is received. On the hardware accelerator, a person detection model based on the image data is invoked. The person detection model outputs first classification result. Based on the first classification result, a person is detected. Second image data is received from the camera. In response to detecting the person, a fall detection model is invoked on the hardware accelerator based on the second image data. The fall detection model outputs a second classification result. A potential fall based on the second classification result is detected. An alert is provided in response to detecting the potential fall.

The invention provides an AI-based factory abnormal behavior identification monitoring method and system. The method comprises the steps of obtaining continuous video stream data collected by a plurality of monitoring devices in a target factory in real time; performing time dimension segmentation and space dimension preprocessing on the continuous video stream data to obtain a multi-frame behavior feature set corresponding to the dynamic behavior sequence, and inputting the multi-frame behavior feature set into a pre-trained abnormal behavior recognition model, performing cross-modal association fusion on the skeleton key point features, the environment associated object contour features and the action trajectory features to generate a target behavior feature vector, performing similarity matching with a preset abnormal behavior feature library, determining an abnormal behavior category, and triggering a corresponding early warning signal according to the behavior type when it is determined that the target behavior feature vector belongs to the abnormal behavior category. And real-time alarm display is carried out. According to the invention, the problem that the existing monitoring system is insufficient in accuracy and real-time performance can be solved.

In some embodiments, an electronic device displays a user interface of a content application in a three-dimensional environment with or without virtual lighting effects applied to the three-dimensional environment depending on whether the user interface is in a mode that does not include virtual lighting or a mode that includes virtual lighting. In some embodiments, an electronic device updates display of interactive user interface elements in response to detecting a respective pose of a respective portion of a user of the electronic device.

The invention provides an improved YOLOv11s safety helmet wearing detection model and an optimization method thereof, and relates to the technical field of computer vision target detection. According to the improved YOLOv11s safety helmet wearing detection model and the optimization method thereof, the improved YOLOv11s safety helmet wearing detection model comprises the following modules: a multi-modal fusion module, a space-time analysis module, a domain adaptation module, a topological optimization module and a dynamic architecture module; and the multi-modal fusion module is used for realizing feature decoupling by adopting channel separation convolution based on input RGB and near-infrared images, fusing visible light and thermal radiation features through a dynamic weight distribution algorithm, implementing affine transformation alignment on multi-scale features by utilizing a spatial transformation network, and generating a multi-modal feature graph. Through fusion of visible light and near infrared spectrum features and implementation of dynamic weight distribution, complementarity of target texture and thermal radiation features under a complex illumination condition is enhanced, and the problem of feature distortion of single-mode data in a strong backlight or low-illumination scene is solved.

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention provides a rehabilitation training action evaluation method and device based on multi-view vision. According to the method, a multi-view camera is adopted to synchronously collect a rehabilitation training image sequence, two-dimensional coordinates of key points of a human body are extracted by improving an HRNet deep learning model, three-dimensional reconstruction is performed in combination with a Gaussian process, motion feature data are extracted, and rehabilitation training quality is evaluated. Accurate motion capture without wearing mark points by the patient is realized, the training constraint feeling of the patient is effectively reduced, the rehabilitation evaluation accuracy is improved, and a personalized rehabilitation scheme can be generated.

The invention discloses a video analysis-based electric power operation personnel behavior identification and early warning system and method, which realize accurate identification and real-time early warning of electric power operation personnel behaviors by combining a video analysis technology with multi-modal data fusion, and effectively improve the safety management level of an operation site. Compared with a traditional safety supervision mode, the method employs a mode of combining deep learning target detection and time sequence behavior analysis, improves the recognition accuracy of operators and safety equipment, fuses the data of equipment worn by the operators with video data, improves the detection precision, and improves the safety supervision accuracy. And misjudgment caused by illumination change, shielding or complex environment is reduced. Besides, high-risk violation behaviors such as no safety helmet wearing, no safety belt fastening, violation climbing, high-altitude object throwing and the like are accurately recognized through the violation detection module, and different levels of alarm measures are adopted according to the severity of the violation behaviors in combination with an early warning feedback mechanism, so that the pertinence and response efficiency of early warning are improved.

The invention discloses a sign language translation method and device based on vision and word feature pre-training alignment, and belongs to the technical field of sign language translation.The sign language translation method comprises the steps that vision features of sign language videos are extracted through a vision encoder, and word text features are extracted through a text encoder; performing comparative learning based on vision and word text features to obtain a pre-trained vision encoder; performing word text mask prediction pre-training on the text decoder; constructing the pre-trained visual encoder and the pre-trained text decoder into a sign language recognition model so as to recognize a word text sequence from the sign language video; and accessing the pre-trained large language model in the domain into a sign language recognition model to construct a sign language translation model, and performing combined fine tuning so as to translate the word text sequence into a natural language text. The sign language recognition and translation method can realize more efficient, accurate and reliable sign language recognition and translation, is applied to the fields of intelligent sign language translation, barrier-free communication, sign language education and the like, and provides more accurate and natural language interaction experience for hearing-impaired groups.

The invention discloses an operating room intelligent monitoring method and system based on monitoring video recognition, and relates to the technical field of medical safety supervision, and the method comprises the steps: collecting a video through an operating room camera array, and constructing an operation region panoramic sequence through a residual neural network and an optical flow field; a double-branch target detection network is adopted to extract the position of a medical worker, the body position of a patient and the characteristics of surgical instruments, and a surgical scene model is constructed; performing trajectory tracking based on skeleton key point extraction and Kalman filtering, and constructing a dynamic graph of the operation process; generating an operation process state report by using the time sequence diagram convolutional network and a multi-head attention mechanism; and comparing the operation specification library through a knowledge distillation algorithm, and carrying out grading recording on abnormal events. According to the invention, efficient abnormal event tracking and recording functions are realized, technical support is provided for operation quality control and safety management, and the overall performance of operating room intelligent monitoring is improved.

An example operation may include at least one of receiving, via a user interface of a device, an activation input from a user to initiate a session, capturing, by a camera of the device, a scan of a body of the user, wherein the capturing comprises recording at least one image and / or at least one video of the user, processing the at least one image and / or video to generate a three- dimensional model of the user comprising measurements and contours of the body, retrieving, from a database, at least one clothing item associated with the user, the at least one clothing item comprising dimensional attributes and texture attributes, rendering, by a graphics processing unit, the at least one clothing item onto the three-dimensional model to generate a visual representation, wherein the rendering simulates draping behavior, movement, and light interaction of the at least one clothing item relative to the three-dimensional model, and displaying, on the user interface, an interactive visualization comprising the visual representation of the three-dimensional model with the at least one clothing item from multiple viewing angles.

The invention discloses a poultry breeding monitoring management system based on big data, and relates to the technical field of poultry breeding supervision, the poultry breeding monitoring management system monitors the environment in a poultry house in real time, immediately triggers a behavior monitoring module when abnormality is detected, generates a specific environmental abnormality score, and combines visual monitoring and infrared thermal imaging to realize the monitoring and management of poultry breeding. According to the method, individual behavior characteristics of each poultry are effectively extracted, an individual behavior data stream is generated, individual potential health risks are identified, a health archive is adopted to accumulate long-term behaviors and environmental data of each poultry, and a time sequence analysis model is utilized to identify individual behavior modes and health change trends. Possible health risks are predicted based on historical data and models, management perspectiveness and disease prevention and control efficiency are effectively improved, meanwhile, abnormal features with the most influence are automatically recognized through comprehensive risk scoring and contribution degree analysis and are pushed to management personnel, the health archive of each poultry is updated in real time, and the management efficiency is improved. And the overall operation efficiency of the farm is improved.

The invention provides a reflective vest wearing state identification method and system based on deep learning, and the method comprises the steps: obtaining a real-time monitoring image of a target scene, extracting a reflective vest region image corresponding to a to-be-detected human body object from the real-time monitoring image, and carrying out the multi-scale feature extraction of the reflective vest region image, generating an initial wearing state feature; based on a pre-trained wearing state recognition model, carrying out regional distribution analysis on the initial wearing state feature to generate a target wearing state feature, and then determining a reflective vest wearing state recognition result of the to-be-detected human body object according to a matching degree between the target wearing state feature and a preset wearing state threshold value; wherein the identification result comprises a normal wearing state, a non-wearing state or a partial shielding state. According to the invention, the recognition accuracy of the wearing state of the reflective vest can be improved.

Disclosed in the present invention is a local feature optimization-based visible light and infrared pedestrian re-identification method. The method comprises: collecting a target area image, the target area image comprising a visible light-mode image and an infrared-mode image; and inputting the target area image into a pedestrian identification model to obtain a pedestrian identification result, wherein the pedestrian identification model comprises a local feature extraction module, a local feature alignment module and a feature collaborative learning module, the local feature extraction module extracts local features of the visible light mode and infrared mode, the local feature alignment module groups and aligns cross-modal local features, so as to establish cross-modal connections, and the feature collaborative learning module enhances each local feature and performs collaborative optimization on the global features and the local features. The present invention can adaptively mine cross-modal local features, directly establish rich alignment relationships for the cross-modal local features, and embed context relationships of global features into the local features, thus remarkably improving the accuracy of pedestrian identification.

The invention relates to the technical field of underwater fish school monitoring, and discloses an underwater fish school monitoring statistical system based on image fusion. According to the system, underwater video streams and sonar reflection intensity data of different spectral bands are acquired through an underwater multi-source image acquisition module, and time-space synchronous multi-modal image data streams are generated through timestamp alignment; a fish school contour reconstruction module is used for segmenting a fish school contour boundary and fusing visible light texture and sonar geometric features to generate an underwater three-dimensional fish school distribution set; the dynamic track mapping module tracks the mass center displacement, calculates the movement rate and the direction deviation angle, and correlates the water area depth to generate a dynamic track topological graph; the behavior anomaly analysis module extracts environment data based on the track mutation node, and detects aggregation density change and direction dispersion to mark an anomaly feature cluster; and the population statistics output module integrates the data, performs classified statistics on population distribution, a quantity threshold value and a migration path overlap ratio, and finally generates a fish school quantity distribution statistics thermodynamic map.

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 discloses an airport bird condition automatic detection and intelligent bird repelling device and method, and the method comprises the steps: collecting airport airspace environment data in real time through a multi-source sensor array, carrying out the preprocessing of the environment data, and generating a fusion data set; a target detection model is constructed based on a deep learning algorithm to perform bird target recognition on the fusion data set, and target feature information is extracted and comprises bird positions, movement tracks, the number and types; calculating a threat level of bird activities to flights through a risk assessment model established based on fuzzy logic and a Bayesian network in combination with airport flight take-off and landing time sequence data and target feature information; triggering a grading early warning signal according to the threat grade, and linking the bird repelling equipment to execute directional bird repelling operation. According to the invention, an airport bird situation automatic detection and emergency prevention and control system is formed, the bird strike risk detection precision, the early warning accuracy and the bird repelling efficiency are improved, and the aviation operation safety is guaranteed.

The invention relates to the technical field of airport bird repelling detection, in particular to an airport bird repelling detection method based on multi-sensor fusion, which comprises the following steps: through cooperative work of a dynamic visual sensor, a millimeter wave radar, an infrared thermal imaging sensor and a microphone array, combining with an improved target detection model and a multi-modal feature fusion algorithm; precise recognition, behavior pattern analysis and dynamic threat evaluation of a bird target are realized, so that the real-time performance, accuracy and intelligent level of bird repelling detection of an airport are remarkably improved.

The invention belongs to the field of cross-modal pedestrian re-identification, and discloses a cross-modal pedestrian re-identification method based on a coal mine scene, and the method comprises the steps: 1, obtaining an identity representing a pedestrian image and an identity tag corresponding to the pedestrian image, and carrying out the preprocessing; step 2, constructing a cross-modal pedestrian re-identification backbone network; step 3, constructing a multi-scale feature enhancement module and adding the multi-scale feature enhancement module into the cross-modal pedestrian re-identification backbone network; 4, determining a loss function, and completing the final training of the cross-modal pedestrian re-identification backbone network; and 5, taking one modal pedestrian image as a query set, taking the other modal pedestrian image as a to-be-retrieved set, and matching the pedestrian image in the query set with the pedestrian image in the to-be-retrieved set to obtain a cross-modal pedestrian re-identification result. According to the method, cross-modal image features can be effectively fused, feature information of pedestrian images can be effectively mined, and the prediction ability and generalization ability of the model in a cross-modal pedestrian image retrieval scene are effectively improved.

In one implementation, a method of body pose estimation is performed at a device including one or more processors and non-transitory memory. The method includes obtaining a plurality of two-dimensional images of a body in a three-dimensional environment at a respective plurality of times. The method includes determining, for each of the plurality of two-dimensional images, the two-dimensional location in the two-dimensional image of one or more joints of the body at the respective plurality of times. The method includes determining, based on the two-dimensional locations, a plurality of three-dimensional locations in the three-dimensional environment of the one or more joints of the body at the respective plurality of times. The method includes determining, based on the three-dimensional locations, a plurality of updated three-dimensional locations in the three-dimensional environment of the one or more joints of the body at the respective plurality of times.

The invention provides a live pig health monitoring method based on multi-source information fusion, and the method comprises the steps: collecting the sound data of a live pig in a natural environment, carrying out the classification and preprocessing of the sound data, forming a sound sample data set, inputting an LSTM neural network model, and carrying out the training, and obtaining a sound detection model; acquiring a live pig attitude image to obtain an attitude sample data set, and inputting the live pig attitude sample data set into the YOLOv5s model for training to obtain an attitude detection model; inputting live pig sound data collected in real time, preprocessing the data, and inputting the data into the sound detection model to obtain sound features; inputting live pig attitude images collected in real time into the attitude detection model to obtain attitude features; measuring the body temperature of the live pig by using an infrared temperature sensor, measuring the weight of the live pig by using a weight sensor, and fusing the sound feature, the posture feature, the body temperature and the weight to obtain a fusion vector; and inputting the fusion vector into a random forest classifier for decision making, and determining the health state of the live pig. According to the method, multi-modal data are fused, comprehensive evaluation and diagnosis of the health condition of the live pigs are facilitated, and the accuracy and robustness of the whole monitoring system are further improved.

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

The invention relates to the field of re-recognition model processing, and discloses a re-recognition model training method and system based on a noise robust prompt learning framework, and the method comprises the steps: generating a global visual feature based on a pre-trained CLIP model, and calculating a context visual feature based on sample neighborhood information; a learnable mapping network is converted into a pseudo-language cue word to construct a self-adaptive cue, and the self-adaptive cue is integrated into a comprehensive text for embedding; through cross-attention module optimization prompting, generalized cross entropy loss and symmetric contrast loss are used to improve robustness to noise labels; through prompt-driven knowledge distillation, learned text embedding is used as a category vector to guide model optimization, and student model output is aligned with a teacher model; training the re-recognition model through identity loss, triple loss and knowledge distillation loss functions; according to the method, the problem that the performance of an existing pedestrian re-identification model is reduced under a noise label is solved, and the identification precision and robustness in a noise environment are improved.

The invention provides a multifunctional health monitoring and early warning system based on radar and vision fusion, and relates to the technical field of intelligent early warning, and the system comprises a feedback receiving module which receives a radar feedback signal and an image feedback signal; the monitoring identification module traverses the target monitoring area to determine monitoring data, and carries out monitoring identification; the abnormity judgment module is used for carrying out fusion analysis on the first monitoring data set and the second monitoring data set, carrying out bidirectional abnormity judgment and generating an early warning instruction; and the risk analysis module uploads the abnormal judgment result to a cloud platform for risk analysis, and sends early warning information to a remote terminal according to the risk level. According to the invention, the technical problem that the accuracy of early warning information is affected due to high false alarm rate caused by environmental infection due to dependence on a single sensor in health monitoring in the prior art can be solved, risk assessment is carried out by combining radar and visual fusion, the accuracy of anomaly recognition is improved, and the accuracy of early warning information is improved. And the early warning real-time performance and accuracy are improved.

The invention relates to an indoor mobile service robot interaction task execution method and device, a storage medium and an indoor mobile service robot system. The method comprises the following steps: acquiring original visual data and original spatial data, respectively processing the original visual data and the original spatial data, acquiring an original audio signal, and converting the original audio signal into a text signal to obtain a dynamic interactive input instruction; analyzing the dynamic interaction input instruction by utilizing a preset large language model to obtain a meta-action sequence corresponding to the to-be-processed task; according to the meta-action sequence, executing each meta-action in the to-be-processed task in sequence, and scheduling the corresponding processed visual data and spatial data when each meta-action is executed; when the execution result of the meta-action is confirmed, the current task execution state is broadcasted through voice, and task interaction is completed. Compared with the prior art, the method has the advantages of complex and wide task processing, high dynamic adaptability, natural interaction, high task execution success rate of an indoor service scene and the like.

Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on facial gestures and limb gestures derived from captured images. An example method includes receiving a series of images captured in real-time. The example method further includes causing generation of a face segmentation and one or more limb segmentations using the series of images. The example method further extracting one or more face segmentations vectors and one or more limb segmentation vectors. The example method further includes causing generation of weighted vectors using the one or more face segmentation vectors and the one or more limb segmentation vectors for each of the one or more the limb segmentations. The example method includes calculating a probability distribution based on the one or more probabilities corresponding to one or more emotions. The example method finally includes determining a predicted emotion based on the probability distribution.

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 an emotional understanding and feedback method for a full-duplex interactive question-answering digital person, and the method comprises the steps: S1, arousing interaction: enabling a digital person of a digital person system to enter an interaction state in a human body detection arousing or voice instruction arousing manner by a user; s2, state analysis: the digital human system judges the state of the user in real time by collecting image data and audio data of the user, and identifies and analyzes the direction, emotion and speech content of the user in real time; and S3, feedback output: the digital human system drives the digital human in voice, expression and body movement through a screen in real time according to the analysis result of the direction, emotion and speech content of the user, and feeds back the result to the user in a manner of adding auxiliary characters, images and videos. The method has the advantages that the emotional state of the user is accurately judged through an advanced algorithm, digital human expressions, body actions and voice feedback are driven in real time according to the emotion and speech content of the user, response is made in time, and a real-person-like real-time interaction scene is achieved.

The invention provides a 3D posture estimation method and system based on space-time double-flow intersection in the technical field of monocular video 3D human body posture estimation, and the method comprises the steps: S1, obtaining a large number of historical human body videos, carrying out the frame-by-frame processing of each historical human body video through a 2D human body posture estimation model, obtaining a 2D coordinate position, and carrying out the frame-by-frame processing of each historical human body video; constructing a joint space-time sequence data set; s2, creating a 3D human body posture estimation model based on a joint semantic coding module, a dynamic graph convolution module, a space-time double-flow cross fusion module and a 3D posture decoding module; s3, training the 3D human body posture estimation model through the joint space-time sequence data set and the loss function; and S4, acquiring a real-time human body video, and reasoning the real-time human body video through the 2D human body posture estimation model and the 3D human body posture estimation model to obtain a 3D human body posture estimation result. The method has the advantages that the precision and robustness of 3D human body posture estimation are greatly improved.

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

Techniques for detecting attributes and / or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and / or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and / or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and / or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and / or the gesture of the pedestrian.