17 results about "Video annotation" patented technology

The invention provides a video labeling method, device and equipment and a computer program product, and relates to the technical field of video processing. The video labeling method comprises the following steps: acquiring a target video; labeling the target video based on a preliminary labeling strategy to obtain video labeling data, the preliminary labeling strategy comprising a labeling tool and / or configuration information for performing video labeling; performing quality evaluation on the video annotation data to determine a quality evaluation result; analyzing problems existing in the video annotation data based on the quality evaluation result, and generating an optimized annotation strategy based on the problems existing in the video annotation data, the optimized annotation strategy comprising an optimized annotation tool and / or configuration information; and marking the target video based on the optimized marking strategy, and updating the video marking data.

This application provides a video annotation method, electronic device, and storage medium. The method includes: acquiring the original video and user-annotated information for the first frame of the original video; generating an initial contour mask based on the SAM3 model and the first frame and its annotation information; determining whether the number of frames in the original video exceeds a preset frame count threshold; if so, dividing the original video into multiple sub-videos; performing temporal propagation annotation on each sub-video sequentially based on the initial contour mask to obtain the initial contour mask for each frame in the original video; and generating the annotation result of the original video based on the original video and the initial contour masks for each frame in the original video. This application generates the initial contour mask based on the SAM3 model, requiring only minimal human interaction to automatically locate the target and identify its state, significantly reducing the workload of manual frame-by-frame annotation and lowering labor costs.

The invention discloses a video annotation method, system and device based on continuous insertion and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a target video and a preset annotation object set, obtaining a time axis, and obtaining a current frame, a historical frame and a rear frame; generating a first recognition result, if the first recognition result is matched with the preset labeling object set, generating a second recognition result, and obtaining a first difference degree; if the first difference degree exceeds a first preset threshold value, inserting initial annotation content into the current frame of the ith time point, performing target identification on the frame behind the ith time point, generating a third identification result, and obtaining a second difference degree according to the third identification result and the first identification result; and if the second difference degree exceeds a second preset threshold value, updating the initial annotation content according to a third identification result, and continuously performing frame-by-frame identification on the target video end point and updating the annotation. The method has the advantages of accurate judgment, self-adaptive adjustment and light-weight operation.

This application relates to a video annotation method, apparatus, device, medium, and product. The method includes: acquiring media information of a video to be annotated, the media information including image data and descriptive text of the video; using a video classification model to determine the confidence level of each category label mapped from the image data to a category label set, and identifying category labels with confidence levels exceeding a preset confidence threshold as target category labels; using category labels with confidence levels below the confidence threshold as pending category labels, and verifying whether the pending category labels are target category labels based on the descriptive text; and annotating the video to be annotated using the target category labels. By using the descriptive text of the video to be annotated to verify and confirm the low-confidence category labels identified in the video classification model, the accuracy of video annotation can be improved, overcoming the long-tail effect caused by insufficient training samples for category labels, and also improving the utilization efficiency of video information resources.

A video annotation method, comprising: by means of key frame matching, automatically determining a key frame comprising a target object from a video to be annotated, and segmenting said video by using the key frame as a video segmentation point; and separately performing target tracking processing on two image sequences located before and after the key frame in the video. Thus, without changing the working principle of the target tracking algorithm, the video annotation method does not need to separately determine whether the first frame in the video to be annotated comprises the target object, thereby achieving the automation of video annotation and effectively improving the efficiency and accuracy of video annotation.

1. The name of the design product: video annotation graphical user interface of electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: for video annotation and quality inspection; in the middle of the interface in the design 1 to design 5 front view, the mark area can be added in the coating area and marked; in the design 6 front view, the mark can be edited in the click pop-up box. 7. Other circumstances that need to be explained: the coating content is the content picture.

This invention discloses a method and system for annotating highway event videos based on structured multi-turn question answering. The method includes quality assessment and video enhancement of the surveillance video stream, determination of the event observation time and segmentation and cropping of the surveillance video stream, identification of scene attributes for each surveillance video segment, construction of a question-answering mapping model to obtain annotation questions and generate JSON-formatted annotation templates, pre-annotation on the annotation templates using a pre-trained video understanding model, human-computer collaborative annotation to generate annotation text based on the pre-annotation results and quality assessment results, storage of the annotation text in a preset file directory, and writing the basic metadata and annotation text storage paths of the same surveillance video segment into a database table. The database table is used for association retrieval of surveillance video segments and annotation text. This method not only improves the efficiency and accuracy of highway event video annotation but also has good interpretability and can be directly applied to highway event video annotation systems.

This application provides a fall detection method, electronic device, and program product based on human posture image prediction. The method includes: acquiring video frames; converting the video frames into human posture images using a preset posture estimation algorithm; predicting the predicted posture image for the next moment based on historical human posture images using a constraint-based generative adversarial network (CGN); using the human posture image corresponding to the video frame of the target person captured at the next moment as the real posture image; determining the individual posture features of the target person based on the real posture image using a preset individual posture recognition strategy; determining the error features between the predicted posture image and the real posture image based on the loss function of the CGN; and determining the detection result representing whether the target person has fallen based on the individual posture features and the error features. This improves upon the problems of weak generalization ability, unstable detection results, and cumbersome video annotation in traditional fall detection methods.

The embodiment of the invention provides a video labeling method and device, electronic equipment, a storage medium and a product, and relates to the technical field of video processing, and the method comprises the steps: obtaining a to-be-labeled video, and carrying out the recognition of the to-be-labeled video, and obtaining a recognition result; generating an attribute description text of each object in the to-be-annotated video and a scene description text of the to-be-annotated video by combining the to-be-annotated video and the corresponding identification result and utilizing the multi-modal large model and the cue word to obtain a to-be-corrected description text; obtaining a correction result obtained by correcting the to-be-corrected description text, and taking the correction result as a final labeling result of the to-be-labeled video; and obtaining a new cue word obtained by correcting the cue word, and adjusting parameters of the multi-modal large model by using a difference between a final labeling result of the to-be-labeled video and the to-be-corrected description text to obtain a new multi-modal large model. In this way, video annotation can be quickly and accurately realized.

The embodiment of the invention provides a method, apparatus, device, medium and product of video annotation, and the method includes: determining a sub-segment to be annotated in a video to be annotated to obtain a target sub-segment; obtaining a first frame annotation result corresponding to a first frame of the target sub-segment; generating an end frame annotation result corresponding to an end frame of the target sub-segment based on the first frame annotation result; generating an annotation result of an intermediate frame of the target sub-segment based on the first frame annotation result and the end frame annotation result, to obtain an annotation result of the target sub-segment to be annotated; and generating a target annotation result of the video to be annotated based on the annotation result of the target sub-segment.

The present disclosure provides a sample data generation method and device, a model training method and device, an electronic device and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of video annotation, the technical field of multi-modal model and the technical field of large model. The specific implementation scheme is: obtaining video data, the video data comprising a plurality of video frames and a text description corresponding to each video frame; extracting a scene description and at least one basic event from the text description; based on the scene description, aggregating the basic events associated with semantics to generate at least one trigger event and a monitoring instruction of the trigger event; determining a time interval set of the trigger event occurrence according to at least one time interval of each basic event occurrence in the video data; for each monitoring instruction, based on the time interval set of the trigger event corresponding to the monitoring instruction, state labeling is performed on the video data to obtain sample data.

The application provides a video labeling method, comprising the following steps: extracting a plurality of video frames from an unlabeled video, displaying at least one video frame in the plurality of video frames to a user through a display interface, obtaining a user labeling result of the user labeling the at least one video frame in the display interface, and obtaining a labeling result of other video frames in the plurality of video frames according to the user labeling result. The method makes the user only need to label a key frame once or even only confirm whether the key frame is labeled correctly in the video labeling process, so that the complete labeling result of the video can be obtained, the labeling operation of the user is greatly reduced, and the video labeling efficiency and the use experience of the user are improved.

This application relates to the field of autonomous driving technology, specifically to a video annotation method, intelligent device, and computer-readable storage medium, aiming to solve the technical problem of how to accurately annotate video streams with timestamps. To this end, this application acquires multiple video frames of the video stream to be annotated. Based on each video frame and its timestamp, it acquires the timestamp-based visual coding features of each video. Based on the timestamp-based visual coding features of multiple video frames, it acquires the annotation results of a preset annotation task for the video stream to be annotated. The annotation results include the timestamp of the preset annotation task, enabling accurate acquisition of the temporal information of the video stream to be annotated, effectively improving the accuracy of the temporal information of the visual coding features, and achieving the fusion of the visual coding features and temporal information of the video stream to be annotated. This effectively enhances the video understanding capability of the video stream and obtains more real-time and accurate annotation results for the video stream to be annotated.

This invention relates to the field of image recognition technology, specifically to a short video annotation method and system, comprising the following steps: acquiring frame feature regions, constructing multi-scale mapping, analyzing dynamic levels, matching semantic tags, and generating a temporal annotation sequence. In this invention, by fusing directional difference and color statistics in short video frames, a joint measure of texture and color changes is achieved, improving the accuracy of regional dynamic feature expression. Edge direction histograms and color moment features extracted by multi-scale windows are standardized and screened using cosine similarity to ensure consistency of regional features across multiple scales and reduce interference. The normalized product of centroid displacement and brightness changes constructs a dynamic intensity distribution, enhancing the discriminativeness of motion level division. Semantic matching combines motion parameters and semantic vectors for joint calculation, improving the semantic association and accuracy of tag generation. Temporal reorganization uses trajectory aggregation to achieve temporal continuity of tags, ensuring the uniformity and stability of multi-frame annotation in both spatial and temporal dimensions.

The application discloses a video labeling method, system, device and medium based on continuous insertion, relates to the technical field of data processing, and comprises the following steps: acquiring a target video and a preset labeling object set, acquiring a time axis, acquiring a current frame, a historical frame and a subsequent frame; generating a first recognition result; if the first recognition result matches the preset labeling object set, a second recognition result is generated, and a first difference degree is acquired; if the first difference degree exceeds a first preset threshold, initial labeling content is inserted into the current frame at the ith time point, target recognition is performed on the subsequent frame at the ith time point to generate a third recognition result, and a second difference degree is acquired according to the third recognition result and the first recognition result; if the second difference degree exceeds a second preset threshold, the initial labeling content is updated according to the third recognition result, and frame-by-frame identification and labeling updating are continuously performed to the end point of the target video. The application has the advantages of accurate judgment, adaptive adjustment and lightweight operation.

1. Name of the product in this design: Graphical User Interface for Video Display in Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The interface is used to fill in video-related information and publish videos. 7. Human-computer interaction method of graphical user interface: In the main view of Design 1, users can click the "Change Cover" control to replace the video cover, add video descriptions, add location information, likes, activities or links and video labels in the editing bar at the bottom of the interface, click the switch control on the right side of the interface to enable the original declaration, and click the "Publish" control to publish the video. In the main view of Design 2, users can click the "Change Cover" control to replace the video cover, add a video description, click the recommended location option to fill in location information with one click, add likes, activities or links and video annotations, click the switch control on the right side of the interface to enable the original declaration, and click the "Publish" control to publish the video. In the main view of Design 3, users can click the "Change Cover" control to replace the video cover, add a video description, click the recommended location option to fill in location information with one click, add the previously used like easter egg, add activities or links and video annotations, click the switch control on the right side of the interface to enable the original declaration, and click the "Publish" control to publish the video. In the main view of Design 4, users can click the "Change Cover" control to replace the video cover, edit and adjust the video description, location information, likes, event links, and video annotations, click the switch control on the right side of the interface to turn off the original declaration, and click the "Publish" control to publish the video. In the main view of Design 5, users can click the "Change Cover" control to replace the video cover, edit and adjust the video description, location information and like bonus content, add activities or links and video annotations, click the switch control on the right side of the interface to turn off the original declaration, and click the "Publish" control to publish the video. In the main view of Design 6, users can click the "Change Cover" control to replace the video cover, edit and adjust the video description, location information, activity links and video annotations, add like easter eggs, click the switch control on the right side of the interface to enable the original declaration, and click the "Publish" control to publish the video. In the design 7 main view, the current input state is for video description, the user can enter text, can click the "# topic" control to add a topic, can also click the "@ mention" control to add a video number to be reminded, and can click the "post" control to post the video after completing content editing. In the design 8 main view, when the user clicks the "add description easier to be recommended…" text input area, enter the design 8 interface change state diagram.

This application provides a video annotation method, apparatus, electronic device, storage medium, and product, relating to the field of video processing technology. The method includes: acquiring a video to be annotated and an identification result obtained by recognizing the video to be annotated; combining the video to be annotated and the corresponding identification result, using a multimodal large model and prompt words, generating attribute description text for each object in the video to be annotated and scene description text for the video to be annotated, obtaining a description text to be corrected; acquiring a correction result obtained by correcting the description text to be corrected, as the final annotation result of the video to be annotated; acquiring new prompt words obtained by correcting the prompt words, and using the difference between the final annotation result of the video to be annotated and the description text to be corrected, adjusting the parameters of the multimodal large model to obtain a new multimodal large model. This enables fast and accurate video annotation.