563 results about "Video recognition" patented technology

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.

The invention provides a multi-mode cooperative sensing power station high-risk operation inspection method and system, and relates to the technical field of video recognition, and the method comprises the steps: activating an unmanned plane and a quadruped robot after a power station operation task is started; starting a video acquisition unit, and establishing a synchronous video stream; carrying out fusion alignment with the global reference coordinate system through an external synchronization signal; inputting the video sequence of the fused view angle into a multi-view angle action behavior recognition network, and establishing a dangerous behavior grade score; auditory data and olfactory data of the quadruped robot are obtained, and linkage abnormity is established; and polling abnormity is reported according to linkage abnormity and dangerous behavior grade scores. Through the method and the device, the technical problem of low inspection efficiency caused by difficulty in comprehensively identifying potential risks in a dynamic environment due to limitation of a single sensing mode is solved, and the inspection efficiency of a power station is improved by fusing multi-mode data, timely finding and processing the potential risks and improving the accuracy of video and audio identification.

A system and a method for modeling local and global spatio-temporal context in a video for video recognition includes obtaining an input feature map and transforming the input feature map using linear functions to generate a spatial feature map and a temporal feature map corresponding to a video. The method further includes generating hierarchical contextual feature maps based on the spatial feature map and the temporal feature map that represent a context of the video at multiple levels of granularity. The method further includes aggregating the hierarchical contextual feature maps based on gating weights to obtain a spatial modulator and a temporal modulator that are representative of an aggregated context across the multiple levels. The method further includes obtaining an output spatio-temporal feature map based on the spatial modulator, the temporal modulator, and a query token associated with the video.

The invention discloses a behavior analysis method based on video recognition, a processor and a storage medium, and belongs to the technical field of data processing, and the method comprises the following steps: obtaining an image and a video clip with an operation behavior in an electronic manufacturing process; performing static behavior recognition on the image through a static behavior recognition model in the fusion model, and extracting potential static illegal behaviors in the image; extracting a skeleton point sequence from the video clip through a dynamic behavior recognition model in the fusion model so as to perform dynamic behavior recognition, and extracting potential dynamic illegal behaviors in the video clip; and outputting a behavior category code according to the static violation behavior and the dynamic violation behavior. According to the behavior analysis method based on video recognition, the processor and the storage medium, the problems that in an existing student operation behavior analysis mode, behaviors in operation are difficult to comprehensively capture in real time, and scoring objectivity is difficult to guarantee are solved.

The invention relates to an active train obstacle detection method and apparatus based on a positioning technique. The active train obstacle detection method comprises the following steps: S1, acquiring an electronic map; S2, correcting initial parameters; S3, performing parameter calibration of a video camera; S4, detecting a train obstacle; and S5, outputting an obstacle recognition result. The apparatus comprises a positioning module, a laser radar detection module, a video recognition module, an operational host and an interface module, wherein the operational host is connected to the positioning module, the laser radar detection module, the video recognition module and the interface module respectively. Compared with the prior art, the invention can increase the obstacle detection rate and reduce report failures and errors.

The invention provides an image text recognition method and system based on a mask diffusion model, a storage medium and equipment, and belongs to the technical field of image or video recognition or understanding. According to the method, multi-scale visual features of an image are extracted through a visual encoder, a mask diffusion decoder is combined, a diversified mask strategy and random character replacement disturbance are adopted in a training stage, denoising loss and auto-reflection loss are calculated respectively, and a model is optimized in a combined mode; in the reasoning stage, starting from a full mask state, a complete text sequence is recovered through multi-round iterative denoising. According to the method, the one-way modeling limitation of a traditional autoregression model is broken through, all-around context-dependent modeling is achieved, an autoreversion error correction mechanism and a block low-confidence mask strategy are introduced, and the recognition accuracy and reasoning efficiency in complex scenes such as shielding and fuzzy scenes are remarkably improved. The method provided by the invention reaches a leading level on a plurality of public data sets, and has the advantages of high precision and high speed.

The invention is suitable for the technical field of hydrographic survey, and provides a river and channel water flow velocity measurement method based on video recognition and CFD simulation, and the method comprises the steps: S1, completing data collection and constructing a data set, S2, building a three-dimensional water flow model of a target river or channel, S3, generating a vertical flow velocity distribution model, S4, carrying out the image preprocessing of an obtained video stream, and S5, carrying out the calculation of a vertical flow velocity distribution model. S5, performing target detection on the preprocessed image and calculating an area of the flow velocity, S6, performing optical flow calculation on the extracted specific area and calculating the surface velocity, S7, establishing a distribution relationship between the surface velocity and the vertical velocity, and S8, calculating the real flow velocity of the river channel according to the surface velocity and the vertical velocity distribution. According to the method, the relation between the cross section vertical flow velocity obtained through simulation and the surface flow velocity obtained through calculation of the optical flow method is established, the real flow velocity is finally obtained, multi-source fusion of data is achieved, and the calculation accuracy and the flow velocity measurement precision are remarkably improved.

The invention relates to the technical field of machine learning, and particularly discloses an intelligent parking guidance and reverse vehicle searching system based on multi-source heterogeneous data fusion. The system comprises a data perception fusion layer, a dynamic prediction decision-making layer and a user service interaction layer, realizes multi-step advanced probability prediction and dynamic optimal path planning of a parking space state through fusion of geomagnetic detection, video identification, payment flow, traffic situation and activity information, and realizes the optimal path planning of the parking space state based on multi-mode induction and live-action AR reverse vehicle searching guidance. And the parking efficiency and the user experience are improved.

The invention discloses a deep counterfeit video detection method fusing ViT and spatial features, belongs to the technical field of video identification, and is used for detecting a deep counterfeit video. The method comprises the following steps: constructing a neural network fusing ViT and spatial features; training a deep counterfeit video detection network; acquiring face data information in the deep forged video; and inputting the processed video information into the trained deep forged video detection network, and outputting whether the video information belongs to a forged video or not. According to the method, the advantages of the convolutional neural network in image counterfeiting detail extraction in video counterfeiting detection are fully utilized, thoughts of orthogonal convolution, an attention mechanism, residual connection and the like are combined, the accuracy of deep counterfeiting video detection is improved while the complexity of the model is maintained, and the detection efficiency of the deep counterfeiting video is improved. And moreover, the method has relatively stable detection performance in videos generated by various counterfeiting technologies.

The invention relates to the technical field of video identification, in particular to a video content structured disassembly analysis method and system based on time sequence segmentation. The method comprises the following steps: firstly, acquiring a video and metadata, and performing time sequence segmentation by using a first artificial intelligence model integrating a space-time attention mechanism and a sliding window mechanism to generate time slices; cutting the video into a plurality of video clips by utilizing an FFmpeg tool, and extracting text description of each clip by adopting a second artificial intelligence model integrated with multi-modal fusion and timestamp embedding to realize semantic alignment of actions and texts; meanwhile, key frames are extracted by using FFmpeg to generate a picture group, semantic analysis is performed by a third artificial intelligence model combining target detection and cross-frame consistency constraint, and an action label is generated; and finally, integrating the time slice, the key frame, the text description and the action tag to generate a complete structured output. According to the invention, the identification precision of the video content can be improved.

The invention relates to the technical field of intelligent operation and maintenance of photovoltaic power stations, in particular to an AI video recognition system for photovoltaic power station equipment inspection, which comprises a data acquisition unit, a data preprocessing unit and a defect recognition unit, and is characterized in that characteristics from one mode are used as query, evidence information serving as keys and values is searched from corresponding area characteristics of other modes, and the data acquisition unit is used for acquiring data; the method is used for synergistically diagnosing compound and early defects with weak or invisible characteristics in a single mode. According to the method, cross-modal collaborative reasoning can be realized: when a suspicious feature is found in one modal, whether evidence features capable of mutually verifying exist in the same position in other modals or not can be inquired, the diagnostic logic of field experts is simulated, different physical phenomena can be associated, and the probability of mutual verification is reduced. Therefore, early-stage or composite defects which are extremely difficult to find in any single mode can be accurately identified. Therefore, the problem that the recognition performance is reduced due to environmental interference such as illumination and shadow can be fundamentally solved.

The invention relates to the technical field of video recognition, in particular to a construction safety real-time high-precision detection method and system based on environmental characteristics. Arranging a plurality of sensors in a to-be-detected area to collect field environment data in real time; analyzing the illumination change of the to-be-detected area through the light and shadow analysis model, calculating a light-safety misjudgment index, and identifying a potential light misjudgment area; a visual processing model is utilized to analyze the worker video, human skeleton key points are recognized in real time, personnel behavior safety indexes are obtained, and potential dangerous behaviors are detected; detecting local wind speed and turbulence conditions and vibration and resonance phenomena of a to-be-detected area in real time through a local breeze and vibration detection model, and evaluating a local environment stability index; and the safety level of the to-be-detected area is calculated in real time by using the light-safety misjudgment index, the personnel behavior safety index and the local environment stability index, so that multi-dimensional and real-time construction safety risk assessment is realized.

The invention relates to the technical field of circuit breaker switch monitoring, and provides a method for monitoring the position state of a circuit breaker switch based on a video recognition technology, and the method comprises the steps: collecting a real-time image of a circuit breaker through a camera device, judging the physical state of the circuit breaker, and determining a first judgment result; acquiring real-time data of the relay protection device, recording the operation state of the circuit breaker, and determining a second judgment result; inputting the first judgment result and the second judgment result into a preset signal response device to generate a corresponding response action; wherein the response action comprises an opening action, a fault action and a closing action.

The invention relates to the technical field of video recognition and analysis, and discloses a video recognition and analysis method based on physical characteristics. Video frame pixels are mapped to a two-dimensional coordinate system with the upper left corner as an original point, and mirror image expansion and median filtering are carried out on a gray level image; constructing a binary image based on a gray threshold value, and analyzing and extracting a target region by using a four-neighborhood connected domain; using neighborhood search and polar angle sorting to close the tracking contour, and generating equidistant re-sampling points based on Euclidean distance and an interpolation method; the curvature of the re-sampling points is estimated through a three-point difference algorithm, and zero denominator is avoided through numerical protection; performing discrete Fourier transform on the curvature sequence to extract a frequency spectrum, and normalizing an amplitude to form a standardized feature vector; and finally, inter-frame similarity is calculated based on the feature vector, and the most similar frame is automatically retrieved. By processing unified data standards in stages, edge noise is suppressed, sampling uniformity is ensured, and feature stability and cross-frame comparability are improved.

The invention discloses an encrypted video identification method in a Tor environment. The method comprises the four steps of collecting flow, extracting an AU-burst length sequence, extracting upstream features and classifying downstream tasks. Firstly, real-time Tor traffic is captured at a network information service center of a local area network entrance, then features are extracted from the real-time Tor traffic by using a corresponding TREFS i T segmentation strategy according to Tor video traffic characteristics to obtain an encrypted ADU-burst length sequence, then noise filtering and secondary feature extraction are performed by using a 1DCNN model, and finally classification is performed by using a random forest. According to the method for identifying the encrypted video in the Tor complex environment, the encrypted video does not need to be decrypted, the video played by the client through the Tor network can be identified through the video traffic characteristics so as to supervise harmful videos, and the method has wide application scenes and good supervision effects.

A system and method for training, assessing, and providing feedback on cardiopulmonary resuscitation (CPR) performance based on at least one video of a CPR training session performed by a trainee on a non-mannequin training object. A preprocessing module is configured to process the at least one video to generate a standardized video. A marking module is configured to use pose estimation to mark points for body movements during the CPR training session based on the standardized video, and a computing module configured to compute body movement parameters for CPR based on the marked points. A classification module implements a machine learning model that classifies CPR compressions on the non-mannequin training object based on the computed body movement parameters, thereby generating compression classifications, wherein the machine learning model is trained to extract CPR-specific features. An editor module maps metrics over the standardized video based on the compression classifications and generates a feedback video based on the mapped metrics. An analysis module identifies deviations from CPR guidelines based on the feedback video and generates analysis results based on the deviations. A feedback module provides performance feedback to the trainee based on the analysis results.

The invention discloses a personnel state monitoring method and system based on a YOLO algorithm, and belongs to the field of image or video recognition. According to the method, a real-time image or video stream is acquired through a camera, human body detection and key point positioning are performed by using an optimized YOLO algorithm, and a motion model is established in combination with human body posture estimation so as to judge the state of a person. According to the optimization algorithm, a CBAM attention mechanism and a multi-scale feature fusion technology are introduced, and the detection precision and robustness in a complex scene are improved. The system supports correction of personnel key point information, and noise is eliminated through historical frame data and a prediction model. For abnormal behaviors, such as falling or illegal intrusion, the system can generate an alarm signal by comparing an action model with a normal behavior sample library and notify a management terminal. According to the invention, efficient and real-time monitoring of the state of the personnel in the natural gas station is realized, and the safety management level is effectively improved.

The invention relates to the technical field of image processing, and discloses a sorting video recognition processing method and system based on YOLO. The method comprises the steps of processing mask data by adopting a YOLO-Inpaint algorithm, embedding image restoration loss calculation in a YOLO network, and inferring a mapping relation of complete article features from incomplete features through adversarial training learning to obtain a restoration feature graph. And calculating an article integrity score based on the repair feature map, and integrating the ratio of the visible pixel area to the predicted total area and the similarity of the repair area and the real area. And the integrity score and the detection confidence coefficient are fused according to the adaptive weight of the shielding degree, and a compensated recognition result is generated. And constructing a time-sequence-labeled identification data stream which comprises an article category, a position coordinate, a shielding state and a confidence coefficient, and outputting a processing report. According to the method, the problems of accuracy and robustness of object shielding recognition in the sorting video are solved, and the recognition success rate and the processing efficiency under the shielding condition are improved.

The invention discloses an agricultural greenhouse water and fertilizer management system and method based on video recognition, and relates to the technical field of computer image recognition, and the system comprises a camera which is used for collecting crop images; the environment sensor is used for collecting water and fertilizer environment data of the agricultural greenhouse; the local host is provided with a crop identification model, and the crop identification model is used for identifying the real-time growth state of crops in the crop image; the cloud server is deployed with a growth decision model and a growth regulation model, the growth decision model formulates a growth control strategy according to the types and growth stages of crops in the agricultural greenhouse and the water and fertilizer environment data, the growth regulation model calculates an income value, and a regulation scheme for the growth control strategy is solved by taking the maximum income value as a target; and forming an adjusted growth control strategy. According to the method, the growth state of the crops is considered on the whole instead of achieving the optimal state of some crops, and the benefit of the agricultural greenhouse is improved.

The invention relates to the technical field of image or video recognition or understanding, and discloses a bus driver driving state evaluation method, which comprises the following steps: acquiring a face image, an electrocardiosignal and voice audio data of a bus driver, performing timestamp alignment and preprocessing, and constructing a multi-mode driving state data set; extracting multi-modal features of the collected data, and mapping the multi-modal features to a unified feature space; fusing multi-modal features through a cross-modal attention mechanism, dynamically adjusting attention weight based on an emotional change difficulty index, and extracting emotional change key features; and predicting a two-dimensional continuous emotion value of the driver by using the fusion features, calculating a long-time-sequence emotion driving risk score based on an emotion stimulation dynamic model, and performing evaluation and early warning of a driving state. The problems of single-mode analysis, lack of long-period early warning and driving state static recognition in the prior art are solved, and the purposes of accurate evaluation, high safety, multi-mode fusion and long-time-sequence prediction are achieved.

The invention discloses a berth positioning system and method based on video recognition, and relates to the technical field of intelligent transportation, and the system comprises a calibration module which generates berth calibration data based on a berth type, and stores the data to a cloud server; the edge calculation service module is configured to judge the spatial relationship between the vehicle and the berth through a berth type adaptive algorithm based on the real-time latitude and longitude coordinates of the inspection vehicle and the berth calibration data; and the vehicle identification module records license plate information when determining that the vehicle is in the berth, and actively snapshots an empty berth image when no vehicle is in the berth and uploads the empty berth image to the cloud. According to the scheme of the invention, the accuracy, environmental adaptability and long-term operation stability of parking state recognition can be remarkably improved, and a comprehensive solution with high robustness and low misjudgment rate is provided for urban intelligent parking management.

The invention provides a spinach cleaning quality detection method based on image recognition, and relates to the technical field of image or video recognition. A closed-loop control framework integrating active detection, cross-modal sensing, physical modeling decoupling and intelligent decision optimization is constructed; cleaning efficiency and quality bottlenecks caused by single information dimension and passive control mode in the prior art are fundamentally solved; the self-adaptive cleaning device has the final beneficial effects that the energy consumption and the water consumption of a unit product are greatly reduced by precisely putting water flow energy according to requirements while the cleaning cleanliness stability under complex working conditions is remarkably improved, and self-adaption and self-optimization of the whole cleaning process are realized.

The invention discloses a large video model training method and a related device, and relates to the technical field of video recognition, and the method comprises the steps: collecting a training video data frame to obtain an image frame, inputting a preset prompt word, a user question and the image frame into a large image model, and obtaining a thinking chain and a question answer. Performing cold start on the video large model based on the thinking chain and the question answer to enable the video large model to have thinking chain output capability; and combining training video data and questions to generate space and time disordered data and thinking chain data. And inputting the three types of data into the model to obtain corresponding outputs, calculating the accuracy of each output, obtaining space and time accuracy reward values, and training the model through a group relative strategy optimization algorithm in combination with a thinking chain consistency reward value to obtain an inference video large model. According to the method, the trained video large model can have thinking reasoning capability based on thinking chain implementation.

The invention discloses an intelligent recognition system for environmental protection violation behaviors based on multi-modal AI, belongs to the technical field of environmental protection monitoring and video recognition, and aims to solve the problems of data fragmentation, incomplete violation recognition, low precision and insufficient supervision efficiency in traditional environmental protection monitoring. The system comprises a data analysis module, a dynamic sampling module, a data preprocessing module and an intelligent identification module. Analyzing the production log data, and generating a sewage discharge sequence of a sewage discharge port; generating sampling parameters in combination with the emission sequence and historical violation characteristics, and extracting video frames to form a monitoring frame sequence; positioning a sewage draining exit to obtain a dynamic monitoring area, and identifying emission frames to construct an emission frame data set; processing key evaluation indexes of emission frames, identifying excessive emission and abnormal frames, marking violation behavior types, and generating a violation identification report; according to the invention, full-dimension and high-precision recognition of environmental protection violation behaviors is realized, a traceable and efficient decision support is provided for environmental protection supervision, and the supervision efficiency is remarkably improved.

The embodiment of the invention provides a multi-mode emotion recognition method and device, electronic equipment, a storage medium and a product, and relates to the technical field of emotion recognition. The method comprises the following steps: acquiring a to-be-recognized audio / video which comprises an audio stream and a video stream, segmenting the audio stream to obtain at least one audio segment, inputting each audio segment into an audio recognition model to obtain an audio recognition result, determining a corresponding video segment in the video stream according to a target audio segment of which the audio recognition result is an emotion result, and inputting the video segments into a video recognition model to obtain a video recognition result, and determining a target emotion result of the to-be-recognized audio and video based on the audio recognition result and the video recognition result. According to the embodiment of the invention, the video emotion recognition is used for assisting the audio emotion recognition to complete the emotion recognition of the audio and the video, errors possibly caused by single audio recognition are avoided, and the recognition accuracy can be improved.

The invention relates to the technical field of video recognition, in particular to a smart home emotion interaction method and system based on a monitoring camera, and the method comprises the following steps: deploying the monitoring camera in a living space, capturing continuous video frames in the living space, and generating preliminary biological feature data; and based on the preliminary biological characteristic data, analyzing expressions and postures of the residents, and generating expression and posture analysis results. According to the invention, facial expressions and posture changes of residents are captured through video streams, a multi-dimensional emotion recognition system is established, and dynamic evaluation of short-term and long-term emotional states is realized. Expression and posture sequences are processed independently, emotion changes can be recognized, and misjudgment caused by singleness of facial features is avoided. The dynamic early warning mechanism improves the recognition precision of the abnormal state through the comprehensive evaluation of the behavior pattern and the emotion trend, so that the detection of the abnormal emotion not only depends on the single-frame image information, but also carries out the judgment in combination with the time dependence characteristic of the behavior sequence.

The invention belongs to the technical field of equipment monitoring, and particularly discloses an excavator loading confirmation method, system and equipment based on video recognition and a medium. The method disclosed by the invention comprises the following steps: firstly, calling a shot excavator loading video; then, the loading moment of the excavator is obtained through a vibration sensor or an excavator loading video; secondly, frame extraction processing is carried out on the loading video of the excavator based on the loading moment, and a loading picture is extracted; thirdly, analyzing and calculating the picture after frame extraction through an OCR (Optical Character Recognition) algorithm, identifying the number labeled on the excavator, and pairing the excavator with the truck; and finally, the pairing information and the photos are uploaded to a remote management platform for storage and counting. According to the invention, the matching of the excavator and the truck during each loading is carried out through video recording and analysis, so that the recording of the truck and the excavator for loading the truck during each loading is realized. The method can be widely applied to loading pairing recording of the excavator and the truck.

The invention relates to the technical field of safety management, and discloses a venue safety emergency large model construction method based on artificial intelligence, comprising the following steps: step 1, deploying a composite sensing device in a preset area of a venue, the composite sensing device comprising an infrared thermal sensing detection module and a video identification module, the infrared thermal sensing detection module is used for collecting human body thermal sensing track data, and the video recognition module is used for collecting human body image data and marking behavior characteristics in the thermal sensing track data and the image data in real time. According to the method, the technical scheme of constructing the dynamic event evolution diagram is adopted, the technical effect of automatically constructing the event causal chain is achieved by mapping the personnel behavior state and the regional position into the diagram nodes and establishing the space-time correlation edge, and compared with the technical scheme of isolated analysis of surface features in the prior art, the method has the advantages that the efficiency is high; the defect that a child is misjudged due to the fact that a binding relation between a guardian moving track and ticket business cannot be associated is overcome.

An example operation may include one or more of storing a safety specification for an industrial equipment and a video of an operation that is performed with the industrial equipment, identifying a plurality of video frames within the video that are associated with the operation that is performed with the industrial equipment, generating a description of the plurality of video frames based on execution of a multi-modal artificial intelligence (AI) model on the plurality of video frames, determining a safety issue with respect to the operation that is performed based on execution of a language machine learning model on the description of the plurality of video frames and text content from the safety specification, and displaying an identifier of the safety issue on a display screen associated with the industrial equipment.

The invention discloses a multi-modal video recognition and low-code complex time sequence intelligent algorithm arrangement method, which comprises the following steps of: firstly, jointly constructing a safety production basic algorithm library by using a multi-modal video recognition technology and experience accumulated in previous projects, then analyzing requirements, determining a requirement function and a mixed algorithm requirement, and constructing a safety production basic algorithm library; a reasonable large model architecture is designed; a perfect time sequence algorithm model is established by deeply analyzing the time sequence dependency relationship under various scenes; machine learning and artificial intelligence technologies are utilized to realize dynamic optimization of an algorithm execution sequence; and finally, a friendly interface of the main body is developed and provided by using a low-code technology, so that the main body can complete the arrangement of a complex time sequence algorithm through simple operation. The invention aims to improve the accuracy of video recognition through multi-modal data fusion, simplify the arrangement process of a complex time sequence intelligent algorithm by using a low-code technology, and accelerate the development and deployment of the algorithm.