2126 results about "Real time video" patented technology

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The present invention is a Large Language Model-Based AI-powered interactive doll that provides personalized content, education, companionship, entertainment and emotional support to children. The doll connects to a large language model (LLM) and uses a microphone, video camera, touch screen and speaker to interact with the child. The integrated LLM provides real-time video analysis capabilities. The accompanying app provides a full suite of customization options for parents to tailor the doll's interactions to their child's needs and preferences. The doll provides a wide range of content, including educational content, homework assistance, entertainment, AI art, video and music generation, companionship and emotional support, and can suggest activities, tell personalized interactive stories, assisting with language learning, play games, and interact with the child in various other ways. The doll can also be used as a monitor, allowing parents to listen in on the child's interactions, and in another embodiment, to livestream video via an internal camera.

The invention discloses a power distribution fusion terminal real-time video reasoning method based on an SDNN, relates to the technical field of intelligent power grid monitoring, and aims to solve the problems of high delay, high bandwidth consumption, low power consumption and the like caused by insufficient utilization of computing power resources of an existing power distribution terminal and dependence on a cloud on video processing in the prior art. And the problem of poor adaptability of the model in a complex power grid scene is solved. Firstly, a power grid special sample library is constructed, and data quality is optimized through time domain key frame extraction and a stratified sampling mechanism; carrying out enhanced training on the lightweight model by adopting transfer learning and multi-modal confrontation; designing an intermediate representation conversion and hierarchical caching mechanism to realize efficient model compiling and heterogeneous hardware deployment; edge-side real-time reasoning is realized based on dynamic frame sampling and statistical threshold detection; finally, a self-adaptive feedback closed loop is driven through reinforcement learning, and incremental updating of the model and dynamic allocation of resources are achieved. The real-time performance of power distribution equipment anomaly detection is remarkably improved, and meanwhile the model memory occupancy rate is reduced.

The invention relates to the technical field of smart communities, and discloses a smart community target identification tracking method based on big data, which comprises the following steps: constructing an omnibearing monitoring network in a community, collecting real-time video and sensor data in the community, obtaining feature data of a target through an edge computing technology, and carrying out target identification tracking on the feature data; transmitting the target feature data to a cloud server through a network, identifying and classifying the target feature data, judging the type and identity of the target, analyzing the current behavior of the target according to a preset target historical behavior pattern library, and determining whether the behavior is abnormal or not; and if the behavior of the target is not matched with the normal mode in the historical behavior mode library, judging that the behavior is an abnormal behavior, and triggering an early warning mechanism. According to the invention, intelligent identification, dynamic tracking and timely early warning of the abnormal behavior of the smart community are realized, and the intelligent level and response efficiency of community safety management are improved.

The invention relates to the field of artificial intelligence and computer vision, and discloses an artificial intelligence physical training exercise system which comprises a data acquisition module, a data preprocessing module, an action modeling module, an action evaluation module and a user feedback module. The data acquisition module acquires real-time video data of a moving target and extracts skeleton key point data; the data preprocessing module performs optimization processing on the skeleton key point data; the action modeling module is used for modeling and predicting the action state of the moving target by utilizing a target constraint dynamic optimization model based on the optimized skeleton data; the action evaluation module generates action scores and optimization suggestions through local action decomposition and action matching analysis; and the user feedback module provides real-time feedback information and generates a training summary report according to the action score. The method can achieve the efficient analysis and precise evaluation of the motion of the moving target in a complex scene, provides personalized optimization guidance, and is widely suitable for physical training and teaching scenes.

The invention relates to the technical field of image communication, in particular to a computer network communication data processing method and system based on artificial intelligence, and the method comprises the following steps: carrying out the continuous frame difference calculation of the data of real-time video frames, collecting the pixel-level change data between two frames, and obtaining a difference data set; according to the method, continuous frame difference calculation is carried out on real-time video frames, pixel-level change data between two frames is captured, storage and transmission occupation of useless data is reduced, and data processing pressure is relieved. And the compression of the difference data further optimizes the size of the data packet, so that the transmission efficiency is improved, and the integrity of video key content is guaranteed at the same time. The compressed difference data packets are analyzed, key visual elements are extracted, and limited transmission resources are distributed to important information parts in a centralized manner by focusing on visual key points such as human faces and actions, so that the pertinence of the transmission process is enhanced.

The invention provides a subway tumble detection method and system based on cloud edge collaboration and an X-CLIP model, and relates to the technical field of subway tumble detection, and the method comprises the steps: obtaining a real-time video stream in a subway, and analyzing the video stream data into frame image data and video stream data; inputting the frame image data into the YOLOv8n-c3xghost neural network model to obtain all human body target area detection frames; performing identification detection on the human body target area detection frame through a BoT-SORT target tracking algorithm to obtain candidate images of abnormal behaviors; inputting the candidate image of the abnormal behavior into an X-CLIP model, outputting an abnormal region fragment classification result of the candidate image, identifying an abnormal region fragment again, determining the falling behavior according to the situation that the falling probability is greater than a set threshold value, outputting a position coordinate where the falling behavior occurs and a corresponding original image, and giving an alarm; wherein the model is deployed at the cloud end and trained, the model sinks to the edge side after being compressed and packaged, and real-time video frame detection is realized at the edge side.

The invention relates to the technical field of video behavior recognition, and discloses a behavior recognition and early warning system for a real-time video stream. The system comprises a spatio-temporal feature modeling module, a behavior fragment extraction module, an anomaly propagation modeling module, a risk area positioning module and an early warning strategy generation module. The spatial-temporal feature modeling module builds a dynamic model based on historical data, captures a skeleton key point three-dimensional coordinate sequence, a motion optical flow vector field and a micro-expression intensity spectrum, and outputs a theoretical behavior mode vector; the behavior fragment extraction module generates a multi-modal difference feature tensor through cross-modal difference analysis; the exception propagation modeling module generates an exception propagation path risk probability distribution cloud picture in combination with spatial constraint and trajectory information; the risk area positioning module identifies a high-risk area and marks a boundary; and the early warning strategy generation module dynamically configures monitoring parameters, starts high-frame-rate micro-expression capture for a high-risk area, and performs a track disturbance test on an adjacent area.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention discloses a video multi-target intelligent detection method and system based on mercuric chloride hardware. A hardware decoding module decodes an input video stream in real time, generates video frames and stores the video frames in a shared memory queue. And inputting the video frame into the YOLOv5 model converted by the mercuric chloride OMG tool, pre-loading a plurality of model instances into a memory by using an ACL interface of the mercuric chloride NPU, calling different model instances through a polling scheduling mechanism, and outputting structured data comprising a multi-target detection frame, a category label and confidence. And carrying out non-maximum suppression processing on the reasoning result, and judging whether the target is in an alarm monitoring area or not by adopting a central point detection method. According to the invention, real-time multi-target intelligent detection of the input video stream can be realized. And the decoded video frames are stored in a shared memory queue, so that efficient data transmission and processing are realized. The multi-model parallel reasoning improves the detection precision, and is suitable for the application scene of real-time video multi-target intelligent detection.

The invention discloses a video analysis-based multi-scene operator violation behavior identification method and system, and belongs to the technical field of intelligent operation safety monitoring and artificial intelligence identification, and the method comprises the steps: collecting a real-time video stream of a multi-scene operation site; recognizing a continuous action time sequence in the real-time video stream by using an action recognition depth model; constructing the continuous action time sequence into an action behavior sequence; the action behavior sequence is constructed into a directed behavior graph with time, space and action labels, the directed behavior graph is compared with a directed behavior graph corresponding to the standard action behavior sequence, and illegal behaviors are recognized; and carrying out multi-mode early warning on the identified illegal behaviors. According to the method, the bottleneck that the traditional image recognition technology is weak in action sequence semantic understanding and poor in environmental adaptability is broken through, and accurate recognition and real-time early warning of illegal behaviors in multi-scene operation are achieved.

The invention discloses a target detection tracking method and system based on automatic detection, and relates to the technical field of target detection tracking, and the method comprises the following steps: carrying out the target position and category labeling of an image through a labeling tool based on an image data set of a typical target, and obtaining a training data set with a label, establishing a target detection model according to a YOLO architecture, and introducing a dynamic weighted loss function; according to the training data set, a training batch is generated by using a hierarchical sampling strategy, an optimized training sample is obtained through an online difficult case mining mechanism, and iterative training is performed on the target detection model based on the generated training batch and the optimized sample; and performing target detection on the real-time video stream by using the trained target detection model to obtain the tracking position and category information of each frame. According to the method, the problem of class imbalance can be effectively solved, the detection capability of rare targets is improved, and meanwhile, the overall performance of the model is improved through difficult case mining.

The invention provides a real-time video translation and audio and picture synchronization method and system based on a multi-modal large model, and relates to the technical field of video translations, and the method comprises the steps: obtaining a source video; extracting the source video based on the multi-modal large model to obtain a multi-modal feature; fusing the multi-modal features through a cross-modal attention mechanism to generate a context semantic vector; translating into a target language text in real time based on the context semantic vector, and processing the translated language text based on the multi-modal features to obtain a translated language sound source; and performing mouth shape adjustment on the source video based on the translation language sound source, and merging the translation language sound source and the mouth shape animation video to obtain a real-time translation video with synchronous sound and picture. According to the method, the limitation of traditional single-modal translation is broken through, and the semantic accuracy of translation is remarkably improved by dynamically aligning the context information through the multi-modal features in combination with a cross-modal attention mechanism.

Methods and systems for real-time video enhancement are provided herein. A current frame of a video stream generated by a client device of a plurality of client devices participating in the video conference is identified during a video conference. An enhanced previous frame corresponding to an enhanced version of a previous frame in the video stream is identified. At least the current frame and the enhanced previous frame are provided as input to a machine-learning model. An output of the machine learning model is obtained. The output of the machine learning model indicates an enhanced current frame corresponding to an enhanced version of the current frame. The current frame is replaced with the enhanced current frame in the video stream.

Techniques for optimizing a bitrate ladder for live streaming are described herein. A method for optimizing a bitrate ladder for live streaming includes receiving client-side input and an origin-side input during a first interval in a timeslot, the client-side input comprising CDN logs, the origin-side input comprising a quality measure, extracting from the CDN logs frequency of requests for each bitrate in a bitrate ladder in the timeslot and the duration of recent stall events for client video players. During a second interval in the timeslot, an optimized bitrate ladder comprising an optimal set of bitrates (OSB) is selected using an optimization function, the optimization function taking as input quality measures and a coefficient value determined using stall information. The optimized bitrate ladder is sent to the origin server for live encoding follow-on segments.

The invention relates to the field of data processing, and provides a 5G-based video call information processing method and system, and the method comprises the steps: continuously obtaining a real-time video frame sequence and 5G network environment perception data in a video call scene, carrying out the multi-dimensional state mapping processing of the 5G network environment perception data, constructing a network transmission adaption model, and carrying out the real-time video frame sequence and 5G network environment perception data. Generating a video coding control instruction based on the network transmission adaptation model, performing content-aware coding conversion on the real-time video frame sequence, and outputting a coding optimization stream; in the transmission process of the coding optimization stream, link state fluctuation information is obtained through a 5G network feedback channel, transmission strategy dynamic calibration is performed on the coding optimization stream according to the link state fluctuation information, and a calibration transmission stream is obtained; and carrying out decoding time sequence alignment processing on the calibration transport stream, generating a visual call output sequence which is synchronous with the time of the original video stream unit, and pushing the visual call output sequence to a receiving end presentation device.

The invention relates to the technical field of data processing, in particular to a smart factory monitoring data dynamic detection method, system and device and a storage medium, and the method is applied to dynamic detection of production monitoring data of production line equipment in a smart factory. Performing anomaly detection, and extracting abnormal equipment in the production line; for each abnormal device, performing quantitative analysis on the abnormal device by using a pre-constructed device abnormality evaluation model to obtain a device abnormality index; acquiring real-time video image information of the abnormal equipment, and extracting operator information of the abnormal equipment based on the real-time video image information; correcting the equipment abnormality index of the abnormal equipment by considering the working experience of the operator information to obtain a corrected equipment abnormality index; according to the invention, comprehensive monitoring of the whole production process can be realized, and problems caused by improper cooperative operation of a plurality of devices can be found.

The invention discloses a construction site safety monitoring method and system based on a large model and RAG, and the method comprises the steps: carrying out the intelligent analysis of real-time video data of a construction region based on a visual large model, and recognizing the behavior safety, violation operation, dangerous facilities and environment risks of construction personnel; adding a time label to the analyzed security event text to form a construction site security event log; reasoning and analyzing the security event log, determining whether security early warning is generated or not, and pushing early warning information; carrying out on-site intelligent voice broadcasting according to the early warning information; through a multi-RAG architecture, a construction site safety log database, a public construction safety management database and an enterprise internal safety management database are constructed, and construction safety information is stored by using a vector database and a graph database. By adopting the technical scheme of the invention, the problems of insufficient real-time monitoring capability, limited intelligent analysis capability and lack of effective knowledge base support in the existing construction site safety supervision are solved.

The invention discloses a construction engineering management quality control system and a risk management method, and relates to the technical field of construction engineering management, and the key point of the scheme is that the system comprises a risk analysis module, an engineering environment monitoring module, an engineering quality detection module and a structure specification examination module. According to the method, dynamic management of project quality and risks is realized by analyzing construction project data and monitoring a project site in real time, the risk level of the construction project is accurately quantified through multi-factor risk assessment, and compared with a traditional method, potential risks can be identified in the early stage of the project, prevention measures can be taken, and the risk level of the construction project can be accurately evaluated. The potential risk occurrence rate is remarkably reduced, the transparency of the construction process is improved through integrated analysis of real-time videos and sensor data, project managers can monitor quality control points in real time, construction strategies are adjusted in time through immediate recognition and analysis of concrete surface defects, the construction process is optimized, and the construction efficiency is improved. And the overall safety and success rate of project management are improved.

The invention relates to the technical field of transformer substation intelligent patrol, and provides a transformer substation three-dimensional fusion patrol method and system based on digital twinborn and autonomous identification. According to the method, a fused three-dimensional model is constructed through multi-source data acquisition and a three-dimensional Gaussian splash algorithm, and in combination with deep learning-based point cloud semantic segmentation and clustering, an equipment-patrol means coverage relationship is generated. Creating a virtual inspection proxy object based on a three-dimensional virtual environment, and controlling terminals such as an unmanned aerial vehicle to collect real-time video image data; the system carries out automatic identification on pictures, automatically completes equipment level alignment and standard point location identification, generates fine control of camera zooming, horizontal rotation, pitching and the like, and realizes standardized view finding and acquisition. By combining an enhanced recognition algorithm, traditional image processing and a deep learning model are fused, model self-evolution is realized through incremental learning, flexible expansion and collaboration of various patrol terminals are supported through a unified interface, and refined, real-time and intelligent patrol operation and maintenance requirements of an intelligent substation are met.

The invention discloses a multi-view child motion coordination ability evaluation system and method based on deep learning, belongs to the technical field of motion evaluation, and solves the problems that child motion evaluation in the prior art mainly depends on manual observation and simple physical testing, and multiple angles and key details of child actions are difficult to synchronously track. The method comprises the following steps: training to obtain a skeleton point detection model and an athletic ability evaluation model, acquiring real-time video streams of personnel entering a field based on an acquisition camera, identifying skeleton key points in a preprocessing data set by the skeleton point detection model, performing multi-person detection on a personnel matching result based on an athletic area division method, and evaluating the athletic ability of the personnel. The exercise ability evaluation model carries out quantitative analysis on multi-person detection results; according to the invention, visual identification and motion state detection technologies are combined, a front view angle and side view angle dual-camera layout is adopted, and a deep learning algorithm is matched, so that automatic children dynamic motion evaluation is realized. The action process can be completely captured, and the detection accuracy and efficiency are improved.

The invention discloses an AR (Augmented Reality) game camera system based on combination of live-action and 3D (Three-Dimensional) game elements, which comprises a live-action acquisition module for acquiring RGB (Red, Green, Blue) images and depth information, and processing the RGB images to form a real-time video stream; the semantic segmentation module is used for carrying out semantic segmentation on the real-time video stream, identifying an interactive surface and establishing a dynamic interactive region topology database; the virtual element generation module is used for constructing a 3D model library based on a game engine and dynamically matching the 3D model library with the live-action collection information through an adapter interface to generate virtual elements; the virtual-real fusion engine adopts a multi-layer rendering pipeline technology to realize seamless fusion of virtual elements and a real environment; the interactive response module is used for realizing interactive response between the player and the virtual object; and the adaptive calibration module is used for adjusting parameters of the SLAM algorithm according to the semantic segmentation result. According to the method, accurate fusion and natural interaction of the virtual elements in a complex real scene are realized, more immersive user experience is provided, and high precision and stability can be kept in different environments.

The invention provides a method and a device for identifying and alarming a safety helmet under a mine and computing equipment, the method comprises the following steps: acquiring a frame image of a real-time video stream from a camera device, the real-time video stream being used for fixedly monitoring a working place under the mine; performing human body detection identification on the frame image to obtain a human body identification area; performing head area positioning on the human body recognition area to obtain a head recognition area; carrying out safety helmet wearing identification detection on the head identification area to obtain a safety helmet wearing identification result; and rechecking the safety helmet wearing identification result based on a semantic segmentation model, and determining whether to output a safety helmet non-wearing alarm signal or not according to a rechecking result. According to the technical scheme of the invention, accurate safety helmet wearing identification can be realized for mines with complex environments and under-mine environments.

Disclosed are a method for estimating the poses of multiple targets, a robot control method and system, and a product. The method for estimating the poses of multiple targets comprises: acquiring real-time video data, which comprises first image frames of multiple targets (S11); obtaining first high-dimensional segmented pixel data on the basis of the first image frames (S12); performing rendering on three-dimensional models of the multiple targets on the basis of a first preset pose, so as to obtain first high-dimensional data (S13); and updating the first preset pose on the basis of the minimization of an error between the first high-dimensional segmented pixel data and the first high-dimensional data (S14).

The invention discloses a video stream adaptive low-delay real-time transmission method and system based on edge calculation. The method comprises the following steps: receiving a real-time video stream from a network camera, creating a pipeline queue, adding timestamp information for each video frame, and setting a queue protection mechanism; the coded video frames are taken out from the input queue, and the frames in the video are processed through hardware acceleration decoding; the resource use condition of the system is monitored in real time; executing a self-adaptive frame skipping decision according to a performance monitoring result; timestamp generation: dynamically calculating a timestamp interval according to an actual processing frame rate; receiving the decoded original video frame and the corresponding timestamp information, accelerating decoding by using hardware, and executing a video coding operation; and packaging and transmitting the coded video data, and providing a standard protocol interface to be connected with a client for playing. According to the scheme, stable low delay and relatively low resource occupation can be kept, and meanwhile, the video quality is remarkably improved.

A touch screen controlled light emitting pet toy that has a light emitting device equipped to direct a point of light on a surface to provide entertainment to a pet and includes movement about multiple axis and is operated by a touch screen computer based device communicably connected to a controller of the toy. The toy is equipped to receive user input from an application with a touch-screen directly or indirectly via a camera that provides live video data to the application and relays user input to the toy further allowingdikect control and pre-recorded pattern control of the light emitting device through low latency touch screen interactions.

The invention relates to the technical field of image processing, in particular to an unmanned aerial vehicle positioning method and system based on machine vision, and the method comprises the steps: obtaining a current frame image and a reference image in a real-time video stream of an unmanned aerial vehicle, generating an initial matching pair set, and calculating the structural consistency of each matching pair, the method comprises the steps of adaptively determining a screening threshold value of a current frame image based on structural consistency, determining a screening matching pair set by utilizing the screening threshold value, evaluating a positioning contribution weight of each matching pair of the screening matching pair set, executing weighted pose calculation based on the positioning contribution weights, and obtaining an instantaneous pose of an unmanned aerial vehicle in the current frame image. And inputting the instantaneous pose as an observation value into a time sequence filtering model, performing time sequence fusion in combination with a motion model of the unmanned aerial vehicle, and outputting the final pose estimation of the unmanned aerial vehicle in the current frame image so as to complete the accurate positioning of the unmanned aerial vehicle. The method improves the accuracy of unmanned aerial vehicle positioning.

The invention discloses a reasoning resource allocation optimization method and system for multi-camera real-time video monitoring, and relates to the field of multi-camera real-time monitoring. According to the invention, through reasonable reasoning of resource allocation and combination of a video frame rate dynamic adjustment strategy, optimization processing of video streams of a plurality of cameras is realized. Particularly, in the method, different reasoning resources are allocated according to the priorities of the detection tasks of different cameras, so that the cameras with abnormal event detection requirements obtain more computing resources, and the cameras without abnormal events use fewer resources, thereby achieving the balance between the optimal performance and the computing resources.

The invention relates to a river channel floating object detection method based on transfer learning and a target detection algorithm, and the method comprises the following steps: obtaining real-time video or image data containing floating objects on the surface of a river channel, and carrying out the preprocessing of the real-time video or image data; selecting a deep learning target detection model pre-trained based on a large-scale image data set, and training by using a data enhancement technology through a transfer learning technology; the preprocessed image is input into a trained deep learning model, the model performs feature extraction, classification and detection frame positioning on the image through a multi-layer convolutional neural network and predicts floating objects, and the confidence degree comprehensively evaluates the accuracy of a target in a detection frame and the accuracy of frame prediction; carrying out parameter personalized adjustment; and analyzing and applying a detection result. The problems that the detection of the river channel floaters is difficult to solve that the floaters are diverse in variety, easy to be confused with other objects and complex in environment are solved, and the accuracy and the real-time performance of the detection of the floaters are remarkably improved.

The invention provides a safety production behavior monitoring method and system based on AI video analysis. The method comprises the steps of collecting a real-time video data stream of a production area; inputting each frame of video image in the real-time video data stream into a target detection model for target detection to obtain a personnel target output by the target detection model and a target position coordinate of the personnel target in each frame of video image; cutting out a local image area of the personnel target in each frame of video image based on the target position coordinate, and performing feature recognition based on the local image area to obtain personnel features; performing comparison on the basis of the personnel characteristics and the personnel standard behavior characteristics to obtain personnel behavior states, and performing track association on the basis of the personnel behavior states corresponding to the continuous multi-frame video images to obtain personnel behavior tracks; and performing safety production behavior monitoring based on the personnel behavior state and the personnel behavior track, and generating an abnormal behavior early warning signal. According to the method and the device, the real-time performance and the accuracy of safety monitoring in a production scene are improved.