933 results about "Video detection" patented technology

The invention discloses an AI generation content detection and review method, device and equipment and a storage medium, and the method comprises the steps: receiving multi-modal input data which comprises text data, image data and video data; calling a large language model to carry out compliance analysis on the text data to obtain an analysis result; when the analysis result is compliance, calling a multi-modal model to identify and detect the image data and the video data, and respectively obtaining an image detection result and a video detection result; and aggregating the analysis result, the image detection result and the video detection result to obtain a content detection result. According to the method, the processing paths are automatically allocated according to the content types, redundant calculation is avoided, hardware consumption is remarkably reduced, the multi-modal detection results are aggregated into structured output, the large-batch content processing efficiency is remarkably improved, calculation resource occupation is greatly reduced, and seamless integration of the detection results and a downstream service system is achieved.

The invention discloses a multi-mode counterfeit video detection method based on a multi-head addition cross attention mechanism, and belongs to the technical field of video counterfeit detection. The method comprises the following steps: preprocessing a video stream, decomposing a single-frame positioning face, and extracting an audio to generate a Mel spectrogram slice; the 3D convolutional network extracts video spatio-temporal features and motion differences, and the filter bank extracts audio features in combination with the residual network; audio features are mapped to a video alignment space through asymmetric projection, the video features are subjected to bidirectional interaction with an audio input multi-head addition cross attention module after being subjected to time sequence coding, and audio dominant and video dominant features are generated and are cascaded and fused with original features; and constructing cross-modal similarity loss constraint feature distribution, and fusing feature dynamic weighting and time sequence compression to output four classification probabilities of audio-visual double true, audio-visual double pseudo, video pseudo-audio true and video pseudo-audio pseudo. The multi-mode counterfeiting recognition precision is improved, and texture abnormity and audio and video mismatch features are captured.

The invention relates to the technical field of video detection, in particular to a monitoring video detection and identification method and system based on deep learning, and the method comprises the steps: obtaining a target monitoring video of a monitoring region based on monitoring camera equipment, obtaining a video frame sequence of the target monitoring video, and carrying out the target screening of the video frame sequence, so as to obtain a video frame set; and performing target detection on each video frame in the video frame set based on a trained target detection model to obtain a target detection result set of each video frame in the video frame set. According to the method, through combination of keyword extraction and a large language model, complex monitoring data can be converted into structured event description, the textualized information not only facilitates management personnel to rapidly know the field condition, but also can provide reference for subsequent event processing, and the event description generated in real time is combined, so that the management personnel can conveniently and rapidly know the field condition. And a manager can make a coping strategy more efficiently.

The invention discloses an intelligent parking space state recognition system and method based on video detection, and belongs to the technical field of parking space state recognition, and the method specifically comprises the steps: collecting the video data of a parking area, carrying out the preprocessing of the collected video data of the parking area, carrying out the modeling of a parking lot background based on a time sequence, and carrying out the real-time updating, based on a background modeling result, a foreground is detected, a foreground area is extracted, contour analysis is carried out on the detected foreground area, the occupation state of each parking space is judged, a real-time parking space distribution diagram of the parking lot is generated according to the occupation states of all the parking spaces, and the occupation or idle state of each parking space is displayed. The intelligent parking space state recognition system based on video detection can keep high accuracy in actual scenes such as complex illumination conditions, complex environments, different vehicle types and angles and the like, helps a driver to quickly find an idle parking space, and reduces traffic jam caused by parking space finding.

A video detection method includes extracting a plurality of video snippets of a target video, and extracting local features corresponding to the video snippets, respectively, based on motion information of a target object in the video snippets. The local features are configured for representing a time sequence inconsistency of the video snippets. The method further includes performing fusion on the local features to obtain a global feature of the target video, determining an authenticity probability of the target object in the target video based on the global feature, and obtaining a detection result of the target video based on the authenticity probability.

The invention discloses a method, a device and equipment for detecting falling during vehicle traveling and a storage medium. Comprising the steps that when a vehicle enters a disaster early warning road section, acceleration data and GPS track data of the vehicle are extracted based on a preset time interval; whether falling conditions are met or not is judged based on the acceleration data change characteristics, and when the falling conditions are met, falling starting time and falling ending time are determined based on the acceleration change time; and in a time period from the falling starting time to the falling ending time, judging that the vehicle falls on the basis of stopping updating of the GPS track data or the vehicle speed is zero. According to the method, the vehicle falling behavior can still be quickly found under the severe weather condition or in the environment with poor light, and the problem of road condition sensing blind areas caused by video detection failure and portal flow feedback delay is solved.

The embodiment of the invention relates to the technical field of video detection, in particular to a video behavior recognition method, device and equipment based on multi-mode large model fine tuning. A specific embodiment of the method comprises the steps of obtaining a video behavior recognition data set and an initial video behavior recognition model; for each piece of video behavior identification data, executing the following steps: determining a key frame image corresponding to a video frame sequence included in the video behavior identification data; storing the key frame image to a preset image storage position; based on the label text template, the key frame image, the image storage path and a behavior label included in the video behavior identification data, a training sample is generated, and the training sample comprises a sample image and sample label data; training the initial video behavior recognition model, and determining the initial video behavior recognition model as a video behavior recognition model; and inputting the target video data into the video behavior recognition model to obtain video behavior text information. According to the embodiment, the training duration of the video behavior recognition model can be prolonged.

The invention provides a video quality detection method based on multi-scene self-adaption. The method comprises the following steps: selecting an area in a video picture, extracting multi-dimensional features of the area, determining the type of a scene where the video picture is located, and dynamically adjusting video detection parameters of the video picture; detecting the quality of the video picture based on the video detection parameters; intercepting an abnormal video picture, and adding the abnormal video picture into the constructed equipment fault template library M; and performing local model retraining on the video quality detection model based on the equipment fault template library M, and detecting the quality of a to-be-detected video picture based on the video quality detection model. On the basis of deep analysis of different scene features, the scene type of the video picture is accurately judged through multi-dimensional feature calculation, and then the video quality detection parameters are dynamically adjusted according to the scene type, so that the detection system can automatically adapt to the optimal detection parameters according to scene changes, misjudgment and missed judgment caused by the scene changes are avoided, and the detection efficiency is improved. And the accuracy of video quality detection in different scenes is remarkably improved.

The invention provides a multi-identity internal aggregation deep counterfeit video detection method, which comprises the following steps of: extracting shallow texture difference information and shallow frequency domain information through a modified Officient-B0 network, designing a random channel single-head attention mechanism to carry out feature fusion, mining inter-frame difference information by using space attention and time attention, and carrying out multi-identity internal aggregation on the inter-frame difference information. The problem of deep counterfeit video detection in a multi-identity complex scene is solved.

The invention relates to a deep counterfeit video detection method and device fusing multi-modal information, and is applied to the technical field of video detection. According to the invention, a multi-head self-attention mechanism is adopted to process a video frame feature vector Xv of a video to be detected and an audio feature vector Xa of an audio MEL spectrogram respectively, and then weighted fusion is carried out on two modal features of the video frame feature vector Xv of the video to be detected and the audio feature vector Xa of the audio MEL spectrogram. Therefore, the accuracy and robustness of the deep counterfeit video detection method are improved. The multi-modal feature fusion can make full use of the complementarity of different modal information to ensure that more comprehensive features are captured, thereby improving the accuracy and robustness of deep counterfeit video detection.

The invention relates to the technical field of video detection, in particular to a video anomaly detection method based on a large language model, and the method comprises the steps: constructing a searchable knowledge base through normal videos; after video clips are extracted from a to-be-detected video, a description text is obtained through the large language model; encoding the description text into a vector, and retrieving in a knowledge base to obtain a plurality of most similar behavior patterns; and inputting the most similar behavior patterns and the description text into the large language model, and reasoning whether the video to be detected is abnormal or not through guidance of a third prompt word. By introducing the large language model, the problem that a traditional video anomaly detection method is lack of interpretation is solved, a detailed natural language reasoning process can be output while anomaly detection is carried out, the background, behavior logic and reasoning basis of abnormal event occurrence are clearly described, and the understandability and decision reference value of a detection result are greatly improved.

The invention discloses an unsupervised deeply-forged face video detection method based on strong enhancement contrast learning, which comprises the following steps: firstly, preprocessing face video data, cutting out a full-size label-free image and a face region image, then preliminarily classifying the label-free data and endowing the label-free data with pseudo labels by using natural physiological characteristics in face features; a strong enhancement contrast learning strategy is adopted, a plurality of basic enhancement operations are randomly combined to generate a strong enhancement view with larger structure change and semantic difference, strong enhancement contrast learning of the face image is completed, and finally accurate detection of a counterfeit face video is realized through diclustering and authenticity discrimination. Compared with an existing unsupervised detection method, the method has the advantages that multi-level feature fusion and strong enhancement contrast learning are combined, deep counterfeit traces can be captured more effectively, higher robustness and generalization ability are shown in complex scenes such as noise and compression, and the method is high in robustness and generalization performance. And high-precision detection of the deeply-forged face video under an unsupervised condition is realized.

The invention discloses a ResNet and LSTM-based multi-dimensional spatial-temporal feature fusion deep counterfeit video detection method and device, and the method comprises the steps: obtaining a face image in a video, and positioning key points in the face image; extracting a spatial feature vector in the face image based on a pre-trained ResNet model; performing feature enhancement on the spatial feature vector extracted by the ResNet model based on an attention mechanism; inputting the enhanced spatial feature vector extracted from each frame of face image into an LSTM network according to a time sequence to obtain a time sequence feature; and fusing the time sequence features output by the LSTM and the spatial features extracted by the ResNet model to obtain a fused feature vector, inputting the fused feature vector into a full connection layer for classification, inputting a classification result into a SoftMax layer to calculate the probability of each category, and obtaining the classification probabilities of forged and real videos. According to the invention, through fusion of the multi-dimensional spatial-temporal features, the face counterfeit content in the deep counterfeit video is effectively detected, and the accuracy and robustness of detection are improved.

The invention belongs to the technical field of multimedia security, and particularly relates to a deep forgery detection method and system based on audio and video multi-mode fusion. The method comprises the following steps: extracting lip motion space-time characteristics from a video stream through dynamic ROI (Region of Interest) cutting, and processing an audio stream through fast Fourier transform and a Mel filter bank in sequence to obtain audio spectrum characteristics; reconstructing and generating a corresponding audio feature based on the spatio-temporal feature of lip motion, and fusing the audio spectrum feature and the generated audio feature by adopting a bidirectional cross-modal attention mechanism to obtain an attention fusion feature; a Mel spectrogram of the audio stream is acquired, and feature maps of different scales are extracted from the Mel spectrogram and the video stream by using a feature extraction model formed by a convolutional layer and are fused to obtain multi-scale features; after the attention fusion feature and the multi-scale fusion feature are flattened, performing weighted fusion after channel dimension splicing, and outputting a forgery probability through a multi-layer perceptron. The video detection performance is improved.

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

The invention discloses a railway freight car part replacement operation video detection system, solves the problem of how to improve the railway freight car maintenance operation detection efficiency, and belongs to the technical field of railway freight car maintenance. The method comprises the following steps: a video processing layer processes a vehicle depot operation video stream collected in real time to obtain a dynamic frame image; the multi-target detection and tracking module detects the dynamic frame image to obtain a state label of the target, determines the first occurrence time and the final disappearance time of the target and the position coordinate of the target, and forms time-space sequence data; the action time sequence analysis module is used for analyzing the state label and the space-time sequence data of the target, filtering out abnormal action nodes and extracting action nodes and corresponding time of a state conversion scene; the component state judgment module verifies the final setting state and the operation process compliance of the component according to the state label and the space-time sequence data of the target; and the decision output layer generates a structured detection report according to the analysis result.

The invention discloses a fake video detection method and system based on potential space learning, and belongs to the technical field of multimedia forensics, and the method comprises the steps: reinforcing the capturing capability of a model for the potential distribution difference of true and false videos through triple contrast learning, combining GRU network modeling time sequence dynamic features and 3D CNN extraction time-space content features, and obtaining a fake video detection result. The joint representation of video time-space domain features is realized, potential spatial features and content features are dynamically fused through an attention mechanism to focus and forge contradictory information, and a global self-attention mechanism of a time encoder is utilized to mine a long-range time sequence dependency relationship. The method overcomes the problems of insufficient generalization ability, weak time sequence modeling and single feature expression of an existing deep counterfeiting detection method, can effectively distinguish a high-quality counterfeiting video from a real video, improves the adaptability to an unknown counterfeiting technology, enhances the detection robustness of a time sequence anomaly and a complex counterfeiting scene, and improves the detection efficiency. And a solution is provided for deep counterfeiting identification.

The invention relates to a self-adaptive control method for a rain and snow mode of rail transit, electronic equipment and a medium, and the method comprises the steps: comprehensively studying and judging a rain condition based on multi-sensor data and numerical weather forecast data, and calculating a weather grade in the rain and snow mode, the multi-sensor data including video detection data, laser radar data and rainfall data; whether a rain and snow mode is set or not is judged according to the weather level, if yes, the current track slip degree is evaluated according to the multi-sensor data, and the GEBR value is adaptively updated based on the slip degree; the CC calculates the running safety braking envelope of the train by using the updated GEBR value, and feeds back a slip detection result and a dynamic parameter adjustment result in real time; and the ATS dynamically adjusts the operation plan of the train according to the slip detection result reported by the CC, and optimizes the operation scheduling of the whole train. Compared with the prior art, the method has the advantages that self-adaptive management of the rain and snow mode is achieved based on the dynamically-adjusted GEBR, and the running safety and reliability of the train in the rain and snow weather are effectively improved.

The invention relates to the field of urban drainage, and discloses an urban drainage pipe network operation safety assessment method and system, and the method comprises the steps: obtaining pipe network operation state data and pipe network physical structure data, and constructing a drainage pipe network GIS database; acquiring a pipeline defect detection image based on CCTV television detection and QV video detection technologies, and importing the pipeline defect detection image into a GIS system; performing model calibration dynamic simulation on the constructed drainage hydraulic model based on the data in the GIS and the real-time operation monitoring data of the drainage pipe network to obtain drainage pipe hydraulic data; performing defect evaluation on the sewage pipe network, the rainwater pipe network and the confluence pipe network by adopting different safety level models based on the drainage pipe hydraulic data; based on the corresponding defect probability, the risk is divided into a first-level risk, a second-level risk and a third-level risk; analyzing the pairwise variable relationship to find key indexes influencing the risk; and matching a corresponding coping strategy based on the key index influencing the risk, and displaying the coping strategy.

The invention relates to a deep pseudo video detection method based on multi-modal comparative learning, and is suitable for the field of deep counterfeit detection. The method comprises the following steps: inputting a to-be-detected video into a trained video authentic identification model, and outputting a detection result of the to-be-detected video; the video authentic identification model comprises a visual encoder which is taken from a trained audio and video contrast learning model and is used for extracting visual features of a face sequence in a to-be-detected video; the audio encoder is taken from the trained audio and video comparison learning model and is used for extracting audio features of the to-be-detected video; the cross-modal feature fusion module is used for fusing the visual features extracted by the visual encoder and the audio features extracted by the audio encoder to obtain a feature fusion result; the spatio-temporal feature extraction module is used for extracting spatio-temporal features from the feature fusion result; the fusion features not only comprise visual features, but also comprise audio features, so that the features are richer; and the classifier is used for classifying the to-be-detected video as a real video or a forged video based on the spatial-temporal characteristics.

The invention discloses a deep counterfeit video detection method based on multi-dimensional feature collaborative modeling, which belongs to the field of computer vision and comprises the following steps of: preprocessing a complete face image by using a face recognition technology to obtain an image of a face local area; respectively extracting frequency domain features and spatial domain features of the face local region; carrying out adaptive weight modulation and nonlinear fusion on the extracted spatial domain features and frequency domain features by adopting a position sensing double-domain fusion module; the contribution degree of the local region in the local-global feature fusion process is adjusted, and a final global fusion feature is obtained; and the loss of each task is automatically weighted and balanced, and the prediction classification of the forged video is realized. According to the method, a multi-dimensional feature fusion framework of local and global and spatial and frequency domains is adopted, so that the detection precision of the deeply-forged video is effectively improved.

The invention discloses a target detection system based on radar and video fusion. The target detection system comprises a monitoring target identification module, a monitoring target tracking module, an image shielding analysis module, an image shielding processing module, an image shielding analysis module, a target speed monitoring module and a target speed analysis module. The method comprises the following steps: identifying a monitoring target in a monitoring area and an action track of the monitoring target, selecting a proper camera to track the monitoring target, judging whether tracking of the camera on the monitoring target is affected by shielding, if so, judging whether other cameras can be used for assisting, and if not, when the monitoring target leaves the shielding range, performing the tracking of the camera on the monitoring target. A monitoring target is recognized through image detection, the monitoring target is tracked through radar detection, and the continuity of the monitoring process of the monitoring target is ensured; according to the invention, when the video monitoring is shielded, the fusion of video detection and radar detection is utilized, so that the accurate monitoring of the target is realized.

The invention provides a power transmission line icing detection method and system. The method comprises the following steps: collecting an icing image, temperature distribution data and sparse point cloud data; performing alignment processing on the sparse point cloud data to generate a distance mapping graph; inputting the icing image into a SegFormer model, and adjusting the expansion rate of a grouping depth separable convolutional layer through a distance mapping graph to obtain a first icing segmentation feature; generating an icing area pseudo label based on the temperature distribution data, and supervising the icing image to be segmented into a second icing segmentation feature through comparative learning; fusing and outputting the first icing segmentation feature and the second icing segmentation feature to obtain an icing feature; and according to the icing characteristics, extracting and complementing the corresponding sparse point cloud data, and constructing a three-dimensional model of power transmission line icing to obtain a power transmission line icing detection result, so that the technical problem of low accuracy of video detection of the icing condition of the power transmission line under the condition of severe weather in the prior art can be solved.

The invention provides a forged face video detection method and device based on multi-modal behavior consistency, electronic equipment, a storage medium and a program product. The method comprises the following steps: extracting voice features, facial expression features and head action features from a video signal to be detected; recognizing voice emotion, facial emotion and semantic emotion; based on the VAD value sequences of the various emotions, emotion consistency features and emotion synchronism features among the various emotions are calculated, and emotion semantic consistency features between the semantic content and the facial emotion and between the semantic content and the voice emotion are calculated; constructing a cross-modal time dependence graph to obtain interaction features; processing the voice features, the facial expression features and the head action features by using a hierarchical attention network to obtain time sequence features; a multi-dimensional fusion feature vector is formed; and processing the fusion feature vector by using a preset binary classifier to obtain a classification result indicating whether the to-be-detected video signal is a fake face video or not.

The invention belongs to the technical field of drainage pipeline defect detection, and particularly provides a drainage pipeline video defect detection method and system based on image quality screening, and the method comprises the steps: collecting and processing a detection image in a drainage pipeline, and constructing an image pair training set and a test set; constructing a GAN-based reference-free image quality initial evaluation model; training and testing the reference-free image quality initial evaluation model to obtain an image block quality evaluation discriminator; combining the image block quality evaluation discriminator with the image information entropy weight matrix to obtain a non-reference image quality final evaluation model; and carrying out quality screening on detection images in the drainage pipeline to be detected based on the non-reference image quality final evaluation model, and carrying out defect detection based on the trained YOLOv8 defect detection model to obtain a defect detection result. According to the invention, by screening out low-quality images, the burden of redundant image frames on subsequent defect detection tasks is effectively reduced, and the video detection speed is improved.

The invention belongs to the technical field of power system data processing, and relates to a work ticket information extraction and work video linkage analysis method, which comprises the following steps of: converting a work ticket into a text format by adopting an OCR (Optical Character Recognition) technology, removing irrelevant information by applying a text cleaning technology, performing named entity recognition and relationship extraction by utilizing a pre-trained language model, and generating a structured data table; key information of the work ticket is extracted, framing processing and compression enhancement are performed on the video, and a target detection model of a convolutional neural network and a time sequence convolutional network is applied to identify figures, equipment and important scenes in the video and analyze behavior actions to obtain a video detection result; structured information in a work ticket is matched with a video detection result, a time axis is established, and task arrangement in the work ticket and actual operation in a video are compared, so that dynamic compliance verification and risk assessment are realized; the efficiency and accuracy of information processing are improved, real-time monitoring and dynamic analysis are also realized, and on-site problems are found and processed in time.

The invention relates to an AI generated video detection method and system fusing global and inter-frame semantics, and belongs to the technical field of artificial intelligence, computer vision and multimedia security. The method comprises the following steps: extracting advanced semantic features of a video frame by using a pre-trained CLIP model; modeling a video overall semantic distribution and narrative structure by adopting a Transform structure through a global semantic branch; capturing inter-frame local semantic mutation in parallel through an inter-frame semantic consistency branch by adopting BiLSTM and an attention mechanism; and finally, carrying out adaptive weighted fusion on discrimination results of the two branches through a dynamic fusion module, and outputting a final detection probability. According to the method, videos generated by multiple generation technologies such as GAN, a diffusion model and Sora can be effectively recognized from the high-level semantic dimension, and the method has high robustness to interference such as video compression and fuzziness and cross-model generalization ability and is suitable for actual scenes such as network content auditing and media evidence obtaining.

A panoramic surveillance video generating apparatus includes: a plurality of image sensors configured to acquire a plurality of surveillance videos; and at least one processor configured to: generate a panoramic video by combining the plurality of surveillance videos, detect an event occurring in the panoramic video, set a partial region of the panoramic video as a surveillance region, output a video of the surveillance region to a virtual channel, and adjust at least one of a quantity of the surveillance region, a size of the surveillance region, and a position of the surveillance region based on an event detection result.

The present invention discloses a method for detecting deep face forged videos based on rPPG signals, belonging to the field of artificial intelligence security. It includes obtaining a face sequence from a video; selecting a specific region of interest of the face and adopting a method based on a single green channel to extract rPPG signals; and using a CNN-based classifier to perform forgery discrimination based on the obtained rPPG signals. The present invention utilizes the characteristic that most current forgery methods are difficult to simulate the human heart rate signal, and detects forged faces by extracting rPPG signals from specific parts of the video face, and can effectively detect face forged videos generated by various forgery methods. At the same time, by adopting an rPPG signal extraction method with a certain ability to resist video compression, the detection performance of the present invention for highly compressed forged videos is improved.

The invention relates to a video detection method and device for an edge algorithm all-in-one machine, equipment and a medium, and the method comprises the steps: obtaining task parameters corresponding to a polling scheduling task for each polling scheduling task; starting a plurality of detection algorithm instances corresponding to each algorithm set, and creating a detection queue for each detection algorithm instance; based on a global framing timetable corresponding to the plurality of polling scheduling tasks, performing framing on video streams collected by a plurality of video input devices corresponding to the plurality of to-be-detected video channel sets to obtain a plurality of video frames, and placing the plurality of video frames in respective corresponding detection queues; and for each detection algorithm instance, continuously extracting the video frame from the corresponding detection queue, and carrying out detection analysis on the extracted video frame. According to the invention, the adaptive matching of the frame taking operation and the detection requirement is realized, and the resource utilization rate and the detection real-time performance are improved under the limited computing power.