69 results about "Video assessment" patented technology

The invention relates to the technical field of video optimization, and discloses a video content adaptive optimization system based on deep learning, which comprises a video extraction module, a video decoding module, a coding adjustment module, a video backtracking module and a video evaluation module, the video extraction module is configured to obtain video feature data according to the motion complexity feature vector, the scene switching feature vector and the texture feature vector, and the video decoding module is configured to decode the video feature data based on an initial deep learning model to obtain video adjustment parameters. The coding adjustment module is configured to obtain a first output video, the video backtracking module is configured to obtain a target deep learning model and obtain a second output video based on a first output video adjustment parameter, and the video evaluation module is configured to judge whether the video quality of the second output video reaches the standard or not; and if not, obtaining a third output video according to the historical input video adjustment parameters. The method provided by the invention has the characteristics of good adaptability and optimization for different input videos.

The invention provides a panoramic video three-dimensional point cloud sparse reconstruction method and device integrated with key frame screening, and the method comprises the steps: obtaining a panoramic video of a target scene, and carrying out the video quality evaluation of the panoramic video, and obtaining a video evaluation result; determining a target resolution of downsampling according to the video evaluation result; carrying out downsampling processing on the panoramic video based on the target resolution to obtain a downsampled second video; for the second video, dynamically adjusting a frame extraction interval based on a preset key frame selection mechanism, and extracting a frame index of the key frame from the second video according to the frame extraction interval to obtain a plurality of frame indexes; and obtaining a plurality of corresponding key frames in the panoramic video based on the plurality of frame indexes, and obtaining the three-dimensional scene sparse point cloud of the target scene based on the plurality of key frames, thereby solving the technical problem of low processing efficiency in the prior art.

The invention relates to a human body posture video evaluation method based on a space-time diagram convolutional network, and belongs to the technical field of computer vision, and the method comprises the following steps: obtaining a human body posture point cloud video, carrying out the preprocessing of the video, and constructing a data set; then, an STGCN model is improved, so that the STGCN model can generate a human body posture evaluation score with high accuracy; the improved STGCN model is trained, and a finally trained network state is stored; and evaluating the human body posture video by using the trained generative network. According to the method, the high-accuracy human body posture evaluation score can be generated, the actual application scene is better met, and an important guiding effect on autonomous rehabilitation evaluation can be achieved.

A no-reference video assessment framework employs a multi-resolution input representation and a patch sampling mechanism on a video having multiple frames 302 to aggregate information across different granularities in spatial and temporal dimensions. The framework effectively models complex spacetime distortions that occur in user generated content-type videos. According to one aspect, the framework embeds video clips 302 as multi-resolution patch tokens using complementary modules. This includes a multi-resolution video embedding module 322, and a space-time factorized Transformer encoding module 324, 326. The multi-resolution video embedding module 322 is configured to encode multi-scale quality information in the video, capturing both global video composition from lower resolution frame and local details from larger resolution frames. The space-time factorized Transformer encoding module 324, 326 aggregates the spatial and temporal quality from the multi-scale embedding input, and is configured to output a quality score 330 for the input video.

The invention relates to the technical field of video interaction, and discloses an intelligent video content analysis and interaction system, which comprises a video extraction module, an interaction action module, an analysis adjustment module, an intelligent recommendation module and a video evaluation module, the video extraction module is configured to preprocess an input video and extract video features to establish a training data set, the interaction action module is configured to capture gesture actions, voice commands and touch actions for watching real-time video images, and the analysis and adjustment module is configured to determine emotion types according to fusion feature vectors. The intelligent recommendation module is configured to determine a first recommendation video according to an emotion type, determine a second recommendation video according to an interaction strategy, and determine a third recommendation video based on a machine learning algorithm according to historical watching data and historical interaction behaviors, and the video evaluation module is configured to determine historical matching similarity; and evaluating the recommended video quality according to the historical matching similarity. The method has the characteristic of intelligently recommending the video.

The invention relates to a training method and device of a text generation video model, equipment and a storage medium. In the training process of the text generation video model, the video evaluation model is introduced, the target video generated by the initial text generation video model is subjected to quality evaluation, and the video quality information is generated, so that the model parameters of the initial text generation video model can be updated through the video quality information; generating a trained target text generation video model; through the mode, the video evaluation model can be used for replacing manual scoring, the manual participation degree is greatly reduced, and the labor cost and the time cost are reduced; moreover, score fluctuation caused by artificial subjective difference can be avoided, accurate video quality information is generated, and stable convergence of training is facilitated.

The invention relates to a breath detection and health assessment device and method. The breath detection device comprises an audio evaluation unit which is configured to evaluate the audio confidence degree of breath detection based on an audio signal; a video evaluation unit configured to evaluate the video confidence of the respiration detection based on the video signal; a model matching unit configured to match at least one breath detection model suitable for performing breath detection based on a vehicle condition; the respiration detection unit is configured to call the at least one respiration detection model to process the audio signal and the video signal based on the audio confidence coefficient and the video confidence coefficient so as to generate a first respiration detection sequence corresponding to the audio signal and a second respiration detection sequence corresponding to the video signal; and a detection fusion unit configured to fuse the first respiration detection sequence and the second respiration detection sequence to generate a respiration classification detection sequence.

A video evaluation method for a video comprising consecutive video frames, comprising the steps of generating optical flow vectors for at least a subset of corresponding pixels between pairs of frames, calculating differences between the corresponding optical flow vectors of consecutive pairs of frames, and evaluating the resulting differences with respect to a predetermined criterion, to identify a corresponding pair of frames for further processing.

A system obtains, by a video evaluator, a video clip generated by a video generator of the avatar generator. The system obtains, by the video evaluator, video features of a target person that the avatar is representing. The system compares the video clip with the video features of the target person using a set of video metrics. The system generates a video evaluation score for the video clip based on a comparison of the video clip and the video features.

This invention proposes a method, system, and computer device for generating text-based videos, relating to the technical field of video processing. The method includes: acquiring user input parameters; generating anchor point prompts and anchor point character diagrams based on the user input parameters; evaluating the anchor point character diagrams until they pass evaluation; extracting information from the anchor point prompts to obtain a storyboard; evaluating the storyboards until they pass evaluation; generating audio and storyboard video based on the storyboards; evaluating the storyboard video until it passes evaluation; post-processing the storyboard video to obtain a post-processed video; and combining the post-processed video with the audio to generate a target video. This invention effectively provides a closed-loop evaluation node for text-based video generation, improving the quality of generated text-based videos.

The invention discloses a video-based omni-directional remote rehabilitation training system, a video-based omni-directional remote rehabilitation training method and a medium, which are characterized in that a digital standardized training video library classified according to four stages of PT physical therapy is established, and more than 700 digital standardized training videos such as joint activity, balance training, gait correction and the like are covered; it is ensured that the training content is scientific and covers the whole period and all directions of rehabilitation training; rehabilitators generate personalized training schemes in combination with rehabilitation scene types (hospitalization / home / community / old-age care institutions) and clinical data and in combination with remote video evaluation of the rehabilitators, and training requirements in different environments are met. By capturing actions of limb joint angles, joint point motion trails and muscle force changes, a training action deviation rate is calculated and is fed back and output to a rehabilitation teacher in a grading manner, so that the training standardability is remarkably improved; rehabilitators can manage multiple patients online at the same time, remotely check training percentage data, adjust schemes and generate rehabilitation notes, and efficient, accurate and personalized rehabilitation guidance services are provided for the patients.

This application relates to the field of biomedical engineering technology. This application discloses a method, device, terminal device and storage medium for dual-foot sole video evaluation based on plantar perfusion imaging, which can improve the accuracy of evaluation results, simplify the measurement process and enhance the patient experience. The method includes obtaining a dual-foot sole video; obtaining a plurality of left plantar rPPG signals and a plurality of right plantar rPPG signals based on the dual-foot sole video; drawing a plurality of dual-foot sole perfusion maps based on the plurality of left plantar rPPG signals and the plurality of right plantar rPPG signals; generating a target image based on the plurality of dual-foot sole perfusion maps; extracting the average value, standard deviation, maximum value and minimum value of the blood perfusion index ratio from the target image and inputting them into a classifier for evaluation processing to obtain an evaluation result, and medical staff refer to the evaluation result to diagnose peripheral artery disease and its severity.

The invention provides a data acquisition processing evaluation method and system based on a video image, and relates to the field of video image processing. The method comprises the following steps: acquiring video data in real time through video acquisition equipment; inputting the image frames marked with the target object into a preset video analysis model in sequence to obtain image analysis data of the image frames; obtaining a character interpretation result of each image frame according to the character interpretation template; displaying the image analysis data of each image frame on a display screen of a display console based on the character interpretation result of each image frame; a display screen of the display console is divided into a plurality of display areas, and each display area is used for displaying character interpretation results of different image element data; and recovering a video evaluation result input by the user based on the display console. The problems that due to the fact that a manual video analysis mode is large in labor intensity, low in efficiency, long in time delay, prone to being influenced by personal subjective factors, large in final result deviation and insufficient in field application intellectualization are effectively solved.

A method of video evaluation for a video comprising successive video frames comprises the steps of generating optical flow vectors for at least a subset of corresponding pixels between frame pairs, calculating a difference between corresponding optical flow vectors of successive frame pairs, and evaluating the resulting differences with respect to a predetermined criterion, to identify respective frame pairs for further processing.

The invention discloses a video processing method, apparatus and device, and a computer readable storage medium. The method comprises the steps of obtaining to-be-processed video information; performing evaluation processing on the to-be-processed video information through an evaluation model to obtain video evaluation result information; and determining target video information based on the video evaluation result information and the target video. According to the method, the to-be-processed video information is evaluated through the pre-trained evaluation model, then the target video information is determined according to the video evaluation result information of the to-be-processed video information and the target video, and the target video information is the wonderful video frame. And the extraction efficiency of the wonderful video frame is improved.

The invention provides a video evaluation method and device based on artificial intelligence, an intelligent agent, electronic equipment, a storage medium and a program product, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, and can be applied to scenes such as content generation based on artificial intelligence. The specific implementation scheme is as follows: in response to a received evaluation request, determining a to-be-evaluated video and an evaluation index of the to-be-evaluated video; under the condition that the evaluation index comprises a time sequence evaluation index, analyzing the to-be-evaluated video to obtain content information used for describing the to-be-evaluated video; based on the content information and the evaluation indexes, evaluation prompt information is generated; and inputting the assessment prompt information and the to-be-assessed video into the multi-modal large model to obtain a time sequence assessment result for the to-be-assessed video.

The application discloses a kind of based on deep learning network's ultra-high-definition video compression damage grade evaluation method, comprising the following steps;Step 1, establish multi-resolution compression damage training set, verification set, test set: step 2, construct the compression damage grade evaluation network of time-frequency domain combination: step 3, train the compression damage grade evaluation network of time-frequency domain combination: step 4, test the accuracy of multi-resolution compression damage test set;Step 5, evaluate the compression damage grade evaluation network of time-frequency domain combination.The application is evaluated to ultra-high-definition video compression damage, solves the problem that compression damage data set production cost is high, not easy to expand, video compression damage evaluation network does not adopt end-to-end design, and generalization ability is insufficient and the problem that ultra-high-definition video evaluation is time-consuming.

Systems and methods in accordance with many embodiments of the invention include a motion evaluation system that trains a model to evaluate motion (such as, but not limited to, gait) through images (or video) captured by a single image capture device. In certain embodiments, motion evaluation includes predicting clinically relevant variables from videos of patients walking from keypoint trajectories extracted from the captured images.

The invention relates to the technical field of intelligent control, in particular to a conference room audio and video light control system. Comprising an audio and video acquisition module, an audio control module, a video control module and a light control module, the audio and video acquisition module is used for acquiring audio and video signals and performing primary audio and video processing; the audio control module analyzes each audio signal to obtain a conference audio evaluation value, and automatically adjusts audio setting to adapt to different conference scenes; the video control module comprehensively analyzes the video image to obtain a conference video evaluation value, and optimizes and adjusts the image quality according to the conference video evaluation value; the light control module adjusts the light environment of the conference room according to the light control value; the audio and video lamplight is automatically adjusted, the complexity of manual operation is reduced, the visual experience, comfort and efficiency of a conference are improved, and the participation sense and interactivity of the conference are enhanced.

The present disclosure provides a method, apparatus, and electronic device for video quality assessment, relating to the field of artificial intelligence technology, particularly video assessment. The implementation scheme comprises: obtaining quality assessment data for a video comprising multiple video segments, wherein the quality assessment data includes a score on at least one quality assessment dimension; determining, for each quality assessment dimension, a score for at least one video segment evaluated on the quality assessment dimension among the multiple video segments; and determining a quality assessment result for the video based on one or more video segments in the at least one video segment having the same predetermined score.

The application discloses a configuration method of a formation performance scheme, and the method is applied to a user terminal device, and the method comprises the following steps: obtaining a take-off scheme determined by a user; obtaining a performance scheme determined by the user; generating a formation flight scheme based on the take-off scheme and the performance scheme; performing flight simulation on an aircraft fleet according to the formation flight scheme, obtaining simulation data, and generating a performance video corresponding to the formation flight scheme according to the simulation data, so that the user can evaluate the formation flight scheme based on the performance video. The application can save communication cost and generate the formation flight scheme based on the user demand.

The present invention discloses a multi-frame fusion video noise evaluation method based on a deep convolutional neural network, which mainly solves the problems that existing evaluation methods cannot accurately fit the real scene, have a narrow noise coverage range and low evaluation accuracy for ultra-high definition videos in video noise evaluation. The implementation scheme is as follows: ultra-high definition videos with different noises are collected through two methods, artificial degradation and real shooting, and they are classified into levels to form a training set; a multi-frame fusion video noise evaluation network is designed, and the training set is used to train this network; the video to be evaluated is grouped into video frames according to the frame sequence, and the grouped video frames are input into the trained network to output the noise level of each frame of the video to be evaluated; the level of the frame with the most outputs is used as the level of the video to be evaluated. The present invention has a wide coverage range and can better fit the real scene. It not only has a high evaluation accuracy for the video noise level, but also has a faster evaluation speed. It can be used in ultra-high definition video noise detection systems.

The application provides a CIPN information fusion method and system based on multi-modal information, comprising: evaluating a subjective questionnaire of a to-be-tested person; evaluating walking speed and gait according to a walking video of the to-be-tested person; evaluating finger fine motor skills of the to-be-tested person; evaluating nerve conduction velocity of the to-be-tested person; evaluating brain function of the to-be-tested person; evaluating brain structure of the to-be-tested person; and constructing information fusion according to the subjective questionnaire, the walking speed and gait, the finger fine motor skills, the nerve conduction velocity, the brain function and the brain structure. Compared with a traditional NCI-CTCAE grading system and an EORTC QLQ-CIPN20 questionnaire, the application overcomes subjectivity and repeatability problems, and improves diagnostic accuracy and treatment effect.

The present application relates to the field of biomedical engineering technology. The present application discloses a plantar video evaluation method, apparatus, terminal device and storage medium based on the arrival time difference of the pulse waves of both feet, which can improve the accuracy of the evaluation results. The method includes obtaining a plantar video of both feet of the target user; extracting the first pixel time domain change signal of each pixel point in the plantar region of both feet of each frame image according to the plantar video to obtain the pulse wave signal corresponding to each plantar region; determining multiple regions of interest in the plantar video based on the pulse wave signal corresponding to each plantar region to obtain multiple target regions of interest; extracting the target rPPG signal corresponding to each foot based on the multiple target regions of interest, and constructing a two-foot time difference wave for reflecting the arrival time difference change of the target rPPG signal corresponding to the two feet; performing evaluation processing based on the two-foot time difference wave to obtain an evaluation result, which is used to assist in the diagnosis of peripheral arterial disease and its severity.

The system according to the embodiment aims to allow participants to create and evaluate music and video in real time. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a video generation unit, a performance unit, and an evaluation unit. The reception unit receives input of music genre and tempo from participants. The generation unit generates music based on the information received by the reception unit. The video generation unit generates video based on the music generated by the generation unit. The performance unit plays the music and video generated by the generation unit and the video generation unit in real time. The evaluation unit evaluates the music played by the performance unit.

The invention discloses a modularized industrial robot intelligent process generation method and system based on video language interactive learning, and aims at solving the technical bottleneck that a traditional industrial robot is complex in programming and difficult in task migration. The method comprises the following steps: analyzing space-time association between a natural language instruction and video demonstration by constructing a video language contrast learning model, and generating cross-task reusable semantic representation; combining a reinforcement learning reward mechanism to fuse sparse task signals and dense video evaluation, and driving adaptive optimization of a strategy model; and dynamically mapping the semantic instruction into an executable workflow of the industrial robot based on a componentized architecture, and verifying logic completeness through an automatic state machine. According to the method, the operation threshold of non-professionals is remarkably reduced, rapid task deployment of cross-entity platforms is realized, the system flexibility and execution precision in a complex manufacturing scene are improved, and a closed-loop self-optimization process control solution is provided for intelligent manufacturing.

The invention relates to the field of video evaluation, in particular to a digital evaluation method for film and television videos, which comprises the following steps of: extracting audio and picture evaluation paragraphs for a target evaluation video, and detecting an alternating current complex coefficient and a reference alternating current coincidence coefficient of each audio and picture evaluation paragraph; according to the alternating current complex coefficient and the reference alternating current coincidence coefficient, determining the category of a corresponding sound and picture evaluation paragraph, and according to the category of each sound and picture evaluation paragraph, determining a paragraph evaluation strategy of the corresponding sound and picture evaluation paragraph; under the evaluation completion condition, determining a paragraph key coefficient according to the interaction effective parameter and the synchronous evaluation coefficient of each audio and picture evaluation paragraph; under the condition that the key analysis is completed, an execution compression strategy of the target evaluation video is determined according to the proportion of the key paragraphs, and the execution compression strategy is compression of the target evaluation video by adopting a dynamic execution strategy or a static execution strategy. And the influence of the compression process is reduced.

A method of video evaluation includes: for a current video frame, generating two or more metrics from the list consisting of an interframe greyscale delta sum of absolute differences between the current and immediately preceding frame, a first interframe colour delta sum of absolute differences for at least a first colour channel between the current and immediately preceding frame, a second interframe colour delta sum of absolute differences for at least a second colour channel different to the first colour channel between the current and immediately preceding frame, for a first colour channel of the current video frame, a delta between mean and current measurements of a property of the first colour channel, the mean being derived over a predetermined period of time, and for a second colour channel of the current video frame, a delta between mean and current measurements of a property of the second colour channel, the mean being derived over a similar predetermined period of time; generating respective z-scores of the generated metrics; summing the z-scores to generate a frame score for the frame; generating a rolling mean of the frame score over a plurality of frames; and identifying the current video frame for further processing if the frame score for the current frame exceeds the rolling mean for a predetermined number of preceding frames.

The present disclosure relates to a system to evaluate an avatar generated by an avatar generator. The system comprises an evaluation module including an audio evaluation module for evaluating audio features and a video evaluation module for evaluating video features. Evaluation of the avatar includes extracting audio and video features from the avatar and applying a set of evaluation metrics for generating audio and video evaluation scores. The scores are combined to generate a final score. For avatar generator evaluation, audio clip and video clip are provided to the audio evaluation module and video evaluation module, respectively. A set of evaluation metrics is applied for evaluation. Each metric can generate a score. All scores are combined to generate a final evaluation score.