243 results about "Video recognition" patented technology

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 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.

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 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 a video recognition-based man-machine cooperative processing method for ditches and rivers in an urban area, and relates to the field of video recognition or understanding, and the method comprises the steps: constructing a data collection and analysis closed loop covering the whole periods before, during and after heavy rainfall: collecting images before and after treatment through an unmanned aerial vehicle and a camera before rainfall, generating a treatment operation influence map for quantitatively evaluating the treatment effect; in rainfall, the fixed camera is used for continuously monitoring the water level and the flow velocity; after rainfall, images are collected again, and the abnormal river reach is recognized. According to the method, camera data is taken as a boundary condition, a hydrological model is driven to perform inversion in combination with a governance influence map, missing heavy rainfall process data is deduced, and finally, accurate attribution of abnormal reasons is realized, and a differentiated governance scheme is generated. According to the method, the problem of monitoring data missing in severe weather is effectively solved, and quantitative evaluation of the treatment effect and scientific diagnosis of the abnormal cause of the river channel are realized.

The invention discloses an analysis method and system based on AI video recognition behavior monitoring, and relates to the technical field of video recognition, and the method comprises the steps: collecting video data, calculating the overall pixel gradient change of adjacent frames, marking preliminary candidate frames, calculating the edge saliency intensity of each frame, and analyzing a salient region point set; clustering and calculating the geometric center point of each cluster as an anchor point value, and forming an anchor point set through the center points of each cluster; according to the method, concentrated extraction of salient points is realized through a density-based clustering algorithm DBSCAN, so that an extraction result has logic uniqueness and local integrity of a salient target, a local feature map is extracted by using a shallow convolutional network, target texture and edge information is effectively captured, bounding box optimization is realized by rotating an IoU loss function, and the robustness of the target is improved. And the fitting precision of the boundary of the salient region is improved.

The invention discloses a video event association reasoning method based on a knowledge graph. The method comprises the following steps: S1, collecting video data and preprocessing the video data; s2, constructing an event graph according to a preprocessing result, and generating a state representation sequence through a graph convolutional network; s3, constructing a DreamerV3 world model, inputting a state representation sequence, and generating an event trajectory set; s4, performing trajectory value evaluation operation on the event trajectory set, and constructing a target path based on an evaluation result; s5, executing a structure alignment operation in the event graph according to the target path, and generating a reasoning knowledge graph; and S6, based on the inference knowledge graph, outputting a video event association result according to a time sequence. According to the method, video identification, map modeling and trajectory prediction methods are fully fused, and an efficient closed loop of key event extraction and map-level causal reasoning is realized.

The invention provides a natural shoreline and mangrove forest supervision method and system based on video recognition, and the method comprises the steps: S1, installing and deploying information collection equipment, and setting a preset position and a cruise plan according to different monitoring targets; s2, accessing original sample data acquired by information acquisition equipment, reinforcing an original sample data set through a three-level data enhancement framework, and constructing a shoreline target sample data set; s3, optimizing by taking the YOLOv8n model as a basic model, and constructing a shoreline target recognition model; s4, constructing a target entity-spatial constraint-ownership state triple verification rule fusion judgment model as a behavior recognition model; and S5, inputting a recognition result of the shoreline target recognition model into a behavior recognition model, judging an illegal behavior through a triple verification rule, and generating early warning information. According to the invention, the working efficiency of natural shoreline supervision can be improved, and a replicable solution is provided for fine management of the coastal zone and the mangrove forest.

The invention discloses a freight yard gantry crane operation path optimization method based on linkage node recognition, and particularly relates to the technical field of freight yard gantry crane operation path optimization, which comprises the following steps: acquiring image data of a target gantry crane operation node based on the video recognition capability of a skyeye platform, extracting continuous actions in the image data through a time sequence, and determining the operation path of the target gantry crane; an action chain is formed, the action chain comprises lifting appliance descending, container moving and vehicle entering and exiting, and whether the target gantry crane operation node meets the completion condition or not is preliminarily judged based on the integrity of the action chain; on the basis of preliminarily identifying the completion condition of the operation node, the verification intervention signal is generated, the natural response behavior of the operation personnel or equipment is collected, the response feature vector is constructed and compared with the preset standard vector, so that whether the node really completes the operation or not is judged, and false path scheduling is prevented from being triggered due to false action.

The invention relates to the technical field of image or video recognition or understanding, and discloses an areca yellows early warning method based on regular economic forest multi-dimensional symptom analysis, in the method, the unique morphology of areca and various morphological changes which are invisible under trees but visible in remote sensing during the period that areca suffers from yellows are fully utilized, and the areca yellows early warning effect is achieved. And the multiple symptoms are combined to form a multi-symptom matching degree, so that the misrecognition problem can be eliminated in an auxiliary manner when specific symptoms exist. When the specific symptoms do not exist, whether the disease of the current areca-nut forest belongs to the infectious disease or not is indirectly judged according to the distribution characteristics of the multi-symptom matching degree in time and space by utilizing the distribution characteristics and the disease course development rule when the areca-nut is cultivated as an economic arbor, and if yes, the disease of the current areca-nut forest belongs to the infectious disease; and considering that the areca yellows occupy a large proportion in the areca infectious diseases, the pathogen detection cannot be infeasible due to entrainment of a large number of irrelevant diseased plant samples when the pathogen detection is carried out at the moment. The above points are combined to realize early warning of areca yellows.

The invention relates to the technical field of nondestructive testing, and discloses an aero-engine endoscopic eddy current testing system based on voice interaction and video recognition, which comprises an endoscopic eddy current integrated probe, a voice input module, a processor, an AI image recognition module, a parameter storage module, a display module and a video playing module. The integrated probe integrates an endoscopic optical unit and an eddy current detection unit, and synchronous acquisition of images and eddy current signals is realized. The AI image recognition module analyzes the real-time endoscopic image and automatically recognizes the type and the position of the workpiece; the processor intelligently calls matched detection parameters from the parameter storage module according to the recognition result, automatically configures the working mode of the probe and can control the video playing module to call and display corresponding operation guidance videos according to voice instructions. According to the invention, a traditional detection mode is changed, and automatic setting of detection parameters and intelligent guidance of a detection process are realized through deep fusion of voice interaction and AI visual identification.

An electronic device includes a display and at least one processor operatively connected to the display. The at least one processor may play a workout video through the display, identify the speed of a workout move included in the workout video, identify an audio signal that is distinct from the workout video, identify a target play speed of the workout video at which the speed of the workout move corresponds to the tempo of the audio signal, and control a play speed of the workout video such that the play speed corresponds to the identified target play speed of the workout video.

The utility model provides a kind of electroplating wastewater hexavalent chromium reduction automatic dosing control device, including pH adjusting pool, reduction reaction pool, sampling pump, video recognition system, control assembly, dosing pump and dosing tank;Reduction reaction pool is connected after pH adjusting pool;Sampling pump is connected with reduction reaction pool by sampling pipeline, and video recognition system is connected after sampling pump, and video recognition system is reconnected to reduction reaction pool by reflux pipeline after video recognition system;Video recognition system is connected with control assembly, and control assembly is connected with dosing device, so that control assembly controls dosing device according to video recognition system signal.The utility model uses video recognition system to replace ORP controller, without accurate control wastewater pH value.

This invention provides a motion estimation-based method for accelerating video recognition, comprising: identifying keyframes and non-keyframes in a video sequence; extracting features from keyframes using a Bayer domain basic model to obtain perceptual features; calculating motion vectors between non-keyframes and a reference frame (the frame preceding the non-keyframe) using a fast motion estimation module, wherein the fast motion estimation module employs a pyramid block structure and performs multi-level matching search from coarse to fine under a GPU parallel architecture; deforming the features of the reference frame using the motion vectors to obtain propagation features; predicting the perceptual residual of the current frame using a perceptual residual correction network, numerically correcting the propagation features using the residual, and outputting the corrected propagation features, wherein the perceptual residual correction network is a lightweight network structure; and performing video recognition based on the corrected propagation features. This invention achieves faster and more efficient video recognition.

The application provides a motor vehicle inspection supervision system based on video recognition, which can reduce the possibility of data falsification, ensure the accuracy of supervision data, and improve the supervision effect of the supervision system. In the application, based on the original inspection item process information of motor vehicle inspection supervision, the content of the item video recognition is combined with the motor vehicle registration information, bypass inspection information and inspection item information for comparative analysis according to the inspection specification and requirements. Meanwhile, the motor vehicle safety technology inspection process is decomposed according to the inspection items, the starting and ending time of each inspection item is monitored through the inspection business information management system, the time point of supervision implementation is set, after the end of the inspection item, the abnormality recognition and analysis are immediately carried out through the motor vehicle inspection supervision module, the illegal evidence is fixed in the first time, and the problem of missing evidence chain in the after-investigation evidence collection is avoided.

The present application relates to the technical field of personnel positioning tracking, and provides a personnel trajectory positioning tracking method and system based on RFID and AI video recognition, which comprises the following steps: acquiring video data in an industrial environment; judging whether an event of a personnel target being blocked by an automated device occurs based on the video data; if the event of the personnel target being blocked by the automated device occurs, acquiring predetermined motion information of the automated device; calculating a predicted reappearance space-time window of the personnel target based on the predetermined motion information and a state of the personnel target before being blocked; processing the video data within the predicted reappearance space-time window, searching for and re-recognizing the personnel target, so as to realize personnel trajectory positioning tracking. The present application has the advantages that it can effectively deal with visual blocking of the personnel target by the automated device, and realize rapid and accurate re-recognition and trajectory tracking after the blocking, thereby improving the continuity and accuracy of personnel positioning tracking.

The system according to this embodiment aims to automate the creation of business records and reduce the burden on the user. [Solution] The system according to the embodiment comprises a recording unit, a capture unit, a recognition unit, a documentation unit, a summarization unit, and a record creation unit. The recording unit records video. The capture unit captures the video recorded by the recording unit at regular intervals. The recognition unit performs face recognition based on the video captured by the capture unit. The documentation unit documents the situation based on the information recognized by the recognition unit. The summarization unit summarizes the information documented by the documentation unit. The record creation unit automatically creates a business record based on the information summarized by the summarization unit.

The application discloses a graphical user interface instruction positioning method and system based on dynamic area search, and belongs to the technical field of image or video recognition. The method comprises the following steps: receiving a natural language instruction, and acquiring a current complete interface screenshot; outputting a structured element set after analyzing elements of a candidate area in the current complete interface screenshot, and calculating each index score; generating a new candidate area, and calculating each index score of the new candidate area; performing quality scoring on all candidate areas; planning and searching the candidate areas, constructing an area search tree, selecting an optimal search path, and outputting an optimal area; and inputting the optimal area and the natural language instruction into a basic multi-modal large model, and outputting a position prediction result of a target element. The application can more accurately position a target interface element corresponding to a user instruction in a complex interface with high resolution, dense elements and a large amount of irrelevant interference information.

A method of detecting presentation attacks based on a finger or face image in a first color space captured by an image capture device. The method includes receiving a first color space image in the first color space, converting the first color space image into a number of additional color space images, wherein each additional color space image is in a color space other than the first color space, and providing the first color space image and the number of additional color space images to a trained attention-leveraged data fusion-based classification system that is configured to determine whether the original image is live or a spoof. Also, a facial or finger video-based biometric authentication system and method employed combined losses during training to train a backbone network for performing vide-based identity verification.

The application relates to the field of video processing, and discloses a video label classification method, system and product based on metadata extraction and intelligent scene division, which comprises the following steps: S1, acquiring an input video and converting the input video into an intermediate format code; S2, separating a video image track, an audio track and a subtitle track; S3, extracting metadata of the video, specifically comprising the following steps: S301, extracting image metadata, comparing the similarity of video intervals based on the image metadata, thereby dividing a video scene and generating a scene visual label; S302, extracting scene audio metadata according to the divided scene, thereby generating a scene auditory label; S303, extracting text metadata according to the divided scene, thereby generating a scene text label; and S4, based on different scene labels, combining the weight components and time points of each scene in the video, and comprehensively obtaining a video label. The application can comprehensively extract and summarize metadata of a video, widen the dimension of a video label, and improve the accuracy of video recognition and matching.

The method comprises the steps of: obtaining target low-resolution video data; preprocessing the target low-resolution video data to obtain a face video image sequence; processing the face video image sequence by using a preset fatigue driving behavior recognition model to obtain a fatigue driving behavior recognition result in the target low-resolution video data; and wherein the fatigue driving behavior recognition model is obtained by updating parameters of a preset model according to a label type and a prediction type of to-be-trained low-resolution video data. The present application can train a fatigue driving behavior recognition model according to a dynamic video, and utilize the time sequence relationship of face features between multiple frames of images in the dynamic video, so as to improve the recognition accuracy of the dynamic video scene to a certain extent.

This invention provides a data compatibility processing method and IoT cloud platform based on smart campuses, relating to the fields of smart campus and data processing technology. In this invention, multiple campus IoT surveillance videos collected by multiple campus IoT terminal devices are subjected to data compatibility conversion operations to form multiple corresponding target campus IoT surveillance videos; based on the corresponding video frame timestamp information, video frame stitching processing is performed on the target campus IoT surveillance video frames included in the multiple target campus IoT surveillance videos to form a corresponding stitched campus IoT surveillance video; using a video recognition neural network formed through network optimization, video recognition processing is performed on the stitched campus IoT surveillance video to output the target video recognition result corresponding to the stitched campus IoT surveillance video. Based on the above, the problem of low efficiency in data compatibility processing in existing technologies can be improved.

This invention provides a method for analyzing the radiomics characteristics of medullary thyroid carcinoma based on ultrasound, relating to the field of image or video recognition or understanding technology. The method includes: obtaining data from thyroid surgeries at various target hospitals to form a backup dataset; filtering the backup dataset to obtain a target dataset; labeling the data in the target dataset with Regions of Interest (ROIs); performing feature extraction and feature filtering on the data in the target dataset to obtain training and validation sets; constructing a target radiomics feature model; calculating radiomics feature scores based on the target radiomics feature model; obtaining a clinical feature model, an ultrasound feature model, a comprehensive model, and a scoring model based on logistic regression analysis; and performing radiomics feature analysis based on the clinical feature model, ultrasound feature model, comprehensive model, and scoring model. This invention solves the problems of low diagnostic accuracy and high requirements for experience of image interpreters in existing technologies for thyroid nodules.

The application relates to the technical field of video recognition, and discloses a course video key frame intelligent recognition method based on an AI visual attention mechanism, which comprises the following steps: acquiring a visual saliency feature map of a video frame and calculating a global attention gravity center coordinate, constructing a spatial second moment tensor by using the visual saliency feature map to determine an anisotropy coefficient, then performing nonlinear weighted processing on the trajectory distribution density in a space-time trajectory space, calculating a second acceleration residual based on the processed trajectory evolution process, and determining a key frame in combination with the trajectory distribution density and the second acceleration residual. The application uses an anisotropy regulation mechanism to suppress non-content dynamic interference, checks the integrity of teaching content generation through the second acceleration residual, solves the problem of lag in semantic turning point capture under a dynamic background, and enhances the semantic density of extracted sequences and the recognition stability.

This invention discloses a method and system for optimizing blasting effects in open-pit mines based on video target recognition, belonging to the fields of mining and image or video recognition. The method includes: acquiring digital borehole design data and blasting design parameters for the target blasting area; collecting video data from before to the end of blasting using a drone; extracting video frames before blasting and identifying unblasted boreholes using a first target detection model; determining the borehole number of each unblasted borehole; extracting the video sequence after blasting, identifying punching targets using a second target detection model, and determining the trajectory of the punching targets using a multi-target tracking algorithm; matching the positions of the punching targets with the unblasted boreholes whose numbers have been matched to determine the borehole number of each punching target; acquiring the actual coordinates, actual hole depth, blasting design parameters, and punching event feature parameters for each punching target; and obtaining the borehole diagnosis result based on a preset diagnostic rule base. This invention can efficiently and accurately diagnose and optimize blasting effects.

The invention belongs to the technical field of image recognition, and discloses a real-time video AI recognition accuracy improving method and system based on an unmanned aerial vehicle. Comprising the following steps: collecting flight shooting video data of an unmanned aerial vehicle in real time and cleaning the data to obtain a high-definition frame video data sequence; carrying out lift-off jitter correction, and outputting jitter correction video data; performing out-frame prediction compensation to obtain out-frame compensation video data; performing real-time scene interference identification, executing confidence offset calibration based on an identified interference scene, and generating oscillation correction video data; removing ghost interference to obtain continuous and stable video data; carrying out artifact filtering, and outputting interference filtering video data; sending the interference filtering video data to an unmanned aerial vehicle identification terminal; the method improves the recognition stability and accuracy, and is higher in scene adaptability and higher in processing real-time performance.

This invention relates to the field of image recognition technology and discloses an image or video recognition method for extracting deterioration features of pre-prepared vegetables. The method includes: acquiring an image and generating a semantic index mask for solid, liquid, and specular reflectance subspaces based on semantic segmentation; constructing a dynamic illumination geometric benchmark using the specular region and calculating the relative color difference vector for the solid state and the relative texture signal-to-noise ratio index for the liquid state; constructing a differential feature manifold space and mapping normalized features to difference vectors; monitoring vector magnitude and direction changes based on topology preservation logic, and generating a deterioration recognition instruction when deviating from the normal topology threshold. This invention achieves relative feature extraction free from environmental illumination interference through a semantic anchor difference and manifold space mapping mechanism, solving the problem of identifying the physical property deterioration of solid and liquid components in pre-prepared vegetables under complex illumination.

The present application provides a kind of face video identification method and system, method includes: obtaining the video frame image sequence corresponding to face video, video frame image sequence is by multiple video frame images, and video frame image is obtained by frame by frame processing to face video;According to video frame image sequence, obtain the human heart rate feature and texture feature in face video;According to human heart rate feature and texture feature, determine the authenticity of face video.The system executes the method.The present application can significantly reduce the probability of identifying fake face video as real face video, can effectively prevent face spoofing attack and fake face video attack, reduce the risk of secret information leakage.