365 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 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 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.

The invention relates to the technical field of video detection, in particular to an anomaly detection method and system fusing large and small models and cooperating with cross-time domain perception. Comprising the steps of configuring a small model, a multi-modal large model and a cross-time domain parameter based on an electric power scene; acquiring a monitoring video stream, calculating a dynamic frame extraction frequency based on a scene change coefficient of continuous video frames of the monitoring video stream, extracting a frame sequence and recording a timestamp of each frame; inputting a frame sequence into the small model to obtain a candidate target, removing redundancy through non-maximum suppression, screening an effective target in combination with a single-frame confidence coefficient and an inter-frame time domain continuous overlapping rate, marking an alarm image and generating a preliminary analysis result; a time-domain-crossing video frame, power scene sensor data and environment data are collected according to a dynamic time window by taking an alarm image timestamp as a center, a visual time-domain feature and a power scene sensor time sequence feature are extracted, and a time-domain-crossing fusion feature is obtained through fusion. The problem of misjudgment and missed judgment caused by single-frame dependence in power scene anomaly detection is solved.

The invention discloses a speaking face generation video detection method and device based on multi-modal global and local collaboration, and the method comprises the steps: carrying out the framing and face region extraction preprocessing of a video, and enabling an input audio to be aligned with an input video frame in a time dimension; a speaking face generation video detection model is constructed and trained, an inconsistent region between video frames is focused on through a preset region attention smooth detection module and a difference capture-time frame aggregation module, and an original audio is input into an audio feature extraction module; carrying out consistency calculation on the input audio and video modal features through a preset audio and video fusion module, fusing the audio and video features so as to discriminate the forged generated video to obtain a video discrimination tag, and then calculating a loss training model; and finally, discriminating a to-be-detected video by using the trained model. According to the method, audio and video features are combined, so that the detection precision and robustness can be effectively improved.

The invention belongs to the field of computer vision, relates to a rockfall monitoring method and system based on visual identification, and aims to solve the technical problems that an existing scheme is difficult to achieve good balance among reliability, accuracy and economy and is poor in application effect. The method comprises the following steps: acquiring video stream data and vibration waveform data of a target area; monitoring according to the vibration waveform data, and when the vibration energy exceeds a preset first threshold value, generating a primary vibration event trigger and extracting video data slices from the video stream data; inputting the video data slices into a target detection network to identify candidate moving targets in the video frames and extract kinematic features; performing spectral analysis on the vibration waveform data to determine a comprehensive confidence coefficient; and counting the number of candidate targets of which the comprehensive confidence exceeds a threshold value in a specific time window, and determining a current rockfall risk level. Based on the method, video detection and vibration detection are combined, and the actual application effect is better.

A system and method for smart video detection is provided herein. The system includes an onboard processing unit having a smart video detection and extraction system. The smart video detection and extraction system recognizes and detects a target of interest within an image and / or video of a target object. The smart video detection and extraction system tags an image of interest within the target of interest to generate one or more flagged images and / or video. The onboard processing unit that sends the flagged images and / or video to a ground segment via a downlink.

The invention discloses an expressway traffic event video detection method, which relates to the technical field of video analysis, and comprises the following steps: acquiring a plurality of types of traffic event samples of an expressway, performing data calibration on an initial expressway traffic event training original data set according to multi-modal preprocessing, and obtaining an expressway traffic event training original data set; generating a highway traffic event multi-mode original training data set; performing spatio-temporal joint feature extraction on the multi-modal original training data set of the highway traffic events to obtain multi-modal spatio-temporal joint feature data, establishing a highway traffic event detection classification model, and evaluating probability distribution vectors of abnormal scores and event categories of the highway traffic events; and verifying the occurrence probability of the traffic event type under the given video stream and radar point cloud by using the probability distribution vector based on the abnormal score of the highway traffic event and the event type. The method has the advantages that the detection speed of the highway traffic incident is increased, and the detection accuracy of the traffic incident is improved.

The invention relates to the technical field of machine vision detection, in particular to a video detection system for conveyor belt deviation and foreign matter accumulation, which comprises a video acquisition module, a self-adaptive illumination compensation module, a multi-scale feature fusion module, a space-time consistency analysis module and a decision output module. Wherein the video acquisition module is used for acquiring an original video stream of an operation area of a conveying belt; the self-adaptive illumination compensation module is used for generating an illumination equalization image through dynamic background separation and self-adaptive gamma correction; the multi-scale feature fusion module is used for extracting and generating a plurality of feature maps; the space-time consistency analysis module is used for outputting a deviation azimuth angle, a foreign matter coordinate and a confidence coefficient; and the decision output module is used for generating a deviation grade, a foreign matter type and an alarm instruction according to a preset threshold value. According to the invention, high-precision identification and classification judgment of the deviation of the conveying belt and the accumulation state of the foreign matters are realized through cooperation of multiple modules, and the intelligent level and the practical response capability of the detection system are improved.

A video detection model training method and apparatus, a video detection method and apparatus, and a device, relating to the technical field of artificial intelligence. The video detection model training method comprises: acquiring a video frame sequence, the video frame sequence comprising a plurality of video frames that are related to a target object and sorted in a chronological order (501); for m video frames in the video frame sequence, perturbing a key point of the target object in the m video frames to obtain a video frame sequence sample, there being an anomaly in the change of a feature associated with the key point of the target object in the plurality of video frames comprised in the video frame sequence sample (502); and training a video detection model on the basis of the video frame sequence sample (503). The present application perturbs key points to simulate phenomena "random jitter and drift of key points" present in fake videos so as to obtain samples, such that video detection models trained by means of the samples have a capability to detect "random jitter and drift of key points", thereby improving the detection accuracy of the video detection models.

A driving assistance device includes a line-of-sight information calculating unit to calculate line-of-sight information of a subject from a vehicle interior video, a target object information calculating unit to detect a target object present around the vehicle from a vehicle exterior video and extract target object information, a visibility calculating unit to calculate a visibility indicating a degree of visual recognition of the detected target object by the subject from the calculated line-of-sight information and the extracted target object information, a collision possibility calculating unit to calculate a collision possibility indicating a degree of collision possibility between the detected target object and the vehicle from the extracted target object information, and an effective collision possibility calculating unit to calculate an effective collision possibility from the calculated visibility and the calculated collision possibility.

The invention discloses an AI generated video detection method and system based on double-branch attention fusion. The method comprises the following steps: synchronously extracting a Patch-level feature sequence and a global-level feature sequence of a video frame by using a pre-trained visual Transform; carrying out local time sequence modeling and independent judgment on the Patch feature sequence through a local space-time artifact branch by adopting a pixel tube Transform; carrying out overall narrative modeling and independent judgment on the frame-level global feature sequence by adopting a global time sequence Transform through a global time sequence consistency branch in parallel; and finally, dynamic weighted fusion is carried out on double-branch discrimination results through an attention fusion module, and a final detection probability is output. According to the method, the AI video generated by advanced models such as Sora and Pika can be effectively identified from two complementary dimensions of local space-time details and global time sequence dynamic, and the method has strong robustness and cross-model generalization ability for interference such as video compression and fuzziness, and is suitable for security scenes such as network content auditing and media evidence obtaining.

The underwater video detection device provided by the utility model freely advances in a drainage pipeline filled with water through the spiral roller, performs initial detection by using the high-definition camera, and sends shot image data to a database through the PLC control system for disease diagnosis and comparison. The system can feed back suspected disease points in real time, the angle of the camera is adjusted through the rotating shaft mechanism and the lifting support, and accurate shooting is ensured; the expansion fixing function of the transparent air bag ensures that the camera is stably fixed at the disease position for detailed shooting. The image data is evaluated by the disease model in the database, so that the accuracy and efficiency of pipeline inspection are improved. According to the design, real-time monitoring, accurate diagnosis and flexible adjustment are integrated, the intelligent level and automation capability of pipeline detection are greatly improved, manual intervention is reduced, the intelligence and accuracy of the detection process are improved, and the reliability of pipeline maintenance is enhanced.

The invention relates to the technical field of network content security, in particular to a network media video sensitive information intelligent identification and early warning system, which comprises a data acquisition module for acquiring network media video data; the video information extraction module performs multi-dimensional analysis on the acquired data to obtain depth features; the AIGC video detection module identifies whether the content is the content generated by the AIGC technology or not according to the depth feature, and if yes, an AI generation mark is marked; the causal inference and evaluation module is used for carrying out causal correlation diagnosis on intention and context, carrying out deep analysis through an anti-fact inference technology, judging whether the intention is sensitive information or not, evaluating dangerousness and obtaining a dangerousness level; and the early warning module takes the minimum intervention cost and the maximum risk control as targets, matches and dynamically generates an optimal intervention scheme from a strategy library in combination with the risk level, and intervenes the sensitive video. Therefore, the problems of incomplete multi-modal information analysis, lack of an AI generation content identification mechanism and the like are solved.

The invention relates to a recorded video authenticity detection system and method, and belongs to the technical field of video detection, and the system comprises a client or a front end which is used for obtaining key frame feature setting sent by a back-end server, determining a key frame based on the key frame feature setting and carrying out feature noise injection in a video recording process, after the video recording is finished, the recorded video is packaged, and the packaged video is sent to a back-end server; wherein the key frame feature setting comprises a feature vector setting mode, and the feature vector setting mode comprises a static setting mode and a dynamic setting mode; and the back-end server is used for receiving the packaged video sent by the client or the front end and verifying the packaged video to obtain a verification result, and the verification result comprises a real recorded video or an unreal recorded video. According to the system, the video recorded by the client can be efficiently verified, and the verification accuracy is improved.

The invention discloses an epileptic seizure video detection method based on a memory enhanced double-branch auto-encoder. The method comprises the following steps: firstly, preparing and preprocessing data; secondly, constructing an ME-DBAE model which comprises a query generation module, a multi-head memory module and a double-branch decoder module; training the model by using training set data, finally inputting a to-be-detected video into the trained model to obtain an epileptic seizure score, and judging whether the video belongs to an epileptic seizure sample or not through a threshold value. By designing the multi-head memory module, diversified normal motion prototypes are stored in parallel, and the limitation that a single memory structure is difficult to adapt to complex normal actions is broken through; and by constructing a double-branch decoder architecture, double tasks of input reconstruction and future frame prediction are cooperatively realized, and meanwhile, space structure and time trend characteristics of normal motion are captured, so that epileptic seizure actions and normal actions are effectively distinguished.

The invention provides a monitoring video detection method and device based on an array camera, equipment and a medium, and the method comprises the steps: obtaining a monitoring video in a screen of at least one terminal through the array camera, and obtaining a panoramic image of the monitoring video and at least one regional detail image; calculating a transformation matrix corresponding to each regional detail image on the panoramic image based on the target matching point set corresponding to the panoramic image and each regional detail image; and based on each transformation matrix, splicing each region detail image to obtain a spliced image, taking the spliced image as a detail magnified image corresponding to the panoramic image, and detecting whether an abnormal event exists in the monitoring video based on the detail magnified image. According to the method and the device, the anomaly detection of the monitoring video in any terminal can be realized without installing any software in the terminal, so that the image resolution of the monitoring video and the detection accuracy of the abnormal event in the monitoring video are improved, and the monitoring convenience of the monitoring video is also improved.

The invention discloses an abnormal sample detection and identification method and system based on evolutionary computation and multi-modal consistency constraint, and belongs to the field of network security and artificial intelligence security. The method comprises the following steps: S1, acquiring and preprocessing input data; s2, multi-modal feature extraction and unified characterization are carried out; s3, optimization feature selection and weight self-adaption are carried out; s4, constructing a cross-modal fusion detection model; s5, abnormal sample risk identification; and S6, result judgment output: generating a final detection label according to a threshold function. The method shows high accuracy and strong robustness in counterfeit image and video detection and abnormal sample elimination, and can be widely applied to the fields of multimedia authentic identification, intelligent security and protection and AI content traceability.

The invention relates to the technical field of information security, in particular to an AI video detection method based on multi-feature branch fusion, and the method comprises the following steps: extracting a frame extraction color image from a video, and obtaining a standardized frame sequence after preprocessing; calculating an inter-frame differential volume; obtaining a time sequence spectrum volume according to the standardized frame sequence; performing bilateral filtering decomposition on the standardized frame sequence to obtain an illumination consistency volume; respectively inputting the three types of volume features into a deep convolutional neural network, and after feature extraction and fusion, outputting an AI forgery probability through a classifier; and comparing the AI forgery probability with a decision threshold to generate a video category label, and calculating an index. According to the method, the multi-dimensional features of the time-space domain, the frequency domain and the physical illumination domain are fused, the potential traces of the deeply-forged video are effectively captured, the stable detection performance is kept under various video quality conditions, the accuracy is improved, and reliable technical guarantee is provided for media information security.

The invention discloses a visual anti-occlusion tracking method based on AIS and video fusion. The method comprises the following steps: synchronously obtaining AIS data and corresponding video data; preprocessing the AIS data, predicting latitude and longitude coordinates of the AIS data, projecting the latitude and longitude coordinates to a video pixel plane, and generating a ship AIS track; meanwhile, identifying a ship bounding box in a video frame by using a video detection algorithm, and tracking by using a three-stage association anti-occlusion tracking algorithm; the similarity between the AIS trajectory and the video trajectory is calculated through a dynamic time warping algorithm, and the correlation matching of the AIS trajectory and the video trajectory is realized by using a Hungary matching algorithm, so that a stable and anti-shielding ship tracking trajectory fusing AIS and video information is obtained. According to the invention, the problem that pure visual tracking is easy to fail under the shielding condition is effectively solved.

The invention discloses a high-risk operation intelligent video detection method and system, and the method comprises the steps: 1, obtaining the field condition of a high-risk operation monitoring region in real time through a camera, collecting an image, and carrying out the frame extraction; step 2, based on the image after frame extraction in the step 1, calculating a confidence coefficient score of a detection frame, and outputting a person head detection result; step 3, based on the head detection result output in the step 2, carrying out safety helmet wearing probability distribution conversion and safety helmet wearing diagnosis judgment on the personnel, if a probability value is greater than a dynamic threshold value, recording a normal state, and returning to the step 1 to carry out next frame image processing; otherwise, turning to the step 4; and 4, based on a judgment result of the helmet wearing diagnosis in the step 3, triggering a sound-light alarm, pushing the sound-light alarm to a management platform, storing an alarm image, and returning to the step 1 for processing the next frame of image. According to the invention, a technical solution with higher intelligent degree and higher reliability is provided for safety management and control of energy enterprises.

The application discloses a behavior recognition method, a model training method, a device and a computer medium, which can be applied to various scenes such as behavior recognition, video detection and intelligent transportation. The behavior recognition method comprises the following steps: acquiring a video segment to be recognized; determining a corresponding RGB image sequence and an optical flow image sequence according to the video segment; determining first feature information based on the RGB image sequence, wherein the first feature information comprises first spatial domain feature information and first time domain feature information of the RGB image sequence; determining second feature information based on the optical flow image sequence, wherein the second feature information comprises second spatial domain feature information and second time domain feature information of the optical flow image sequence; and determining a behavior recognition result corresponding to the video segment by using the first feature information and the second feature information. The time domain feature and the spatial domain feature in the video frame are analyzed, and the behavior of a target object in the video frame is analyzed, so that the accuracy of target object behavior recognition is improved.

The invention relates to the technical field of video detection, in particular to a video-based preheater crust detection method, which comprises the following steps of S1, acquiring a video image in a preheater in real time; s2, the collected video images are preprocessed; s3, extracting the characteristics of the inner wall of the preheater; s4, on the basis of the extracted features, constructing a skinning detection model based on a random forest, and training the model through a large amount of historical labeled sample data; s5, analyzing and processing the video image in real time; and S6, storing the collected video image and the detection result, and establishing a database. According to the method, multiple features are comprehensively considered, the random forest optimization algorithm is used for constructing the skinning detection model, the problem that detection is incomplete due to the fact that a traditional method only depends on a single feature is solved, the grey wolf optimization algorithm is further used for optimizing parameters in the random forest, and the accuracy of the skinning detection model is improved.

The application discloses a kind of car coupler centering state video detection method and device, system, storage medium, comprising: S1, the double-marking corner point coordinate value of car coupler different deflection angle is acquired offline, and trajectory curve is fitted by polynomial;S2, car coupler image is collected online, and car coupler double corner point is extracted by corner point and straight line detection algorithm, and the centering state of car coupler is calculated.Adopting the technical scheme of the application, it has the characteristics of high detection real-time, can effectively improve the marshalling efficiency and operating safety of train.

A video system comprises a video overlay processor. The video overlay processor is configured to receive a video stream from a camera system in real time during operation of a platform. The video overlay processor is configured to overlay a first pattern and a second pattern on an alternating and repeating basis onto frames sequentially in the video stream in real time to form a modified video stream. The video overlay processor is configured to display the modified video stream in real time on a display system, wherein the first pattern and the second pattern in the frames in the modified video stream are perceptually hidden on the display system in response to the frames in the modified video stream being displayed without interruption.

The embodiment of the invention is applied to the technical field of video detection, and provides a video brightness detection method, a video brightness detection system and electronic equipment. The electronic device obtains a video file. Wherein the video file comprises a plurality of to-be-detected images, and each to-be-detected image comprises a target object. Then, the electronic equipment calculates a reference brightness value corresponding to each to-be-detected image in the video file, and calculates position information of a target object in each to-be-detected image; and then, the electronic equipment analyzes the video file according to the reference brightness values corresponding to the plurality of to-be-detected images in the video file and the position information of the target object in the plurality of to-be-detected images to obtain a brightness convergence result of the video file. Wherein the reference brightness value is determined based on color information of a target object in the to-be-detected image, and the brightness convergence result is used for evaluating the brightness convergence condition of the video file. According to the method and the device, the smoothness of video brightness convergence is accurately evaluated.

The utility model discloses an aged appearance sorting machine, which relates to the technical field of capacitor and resistor production and comprises a workbench, the top end of the workbench is sequentially provided with a video detection mechanism, a recovery mechanism, a feeding mechanism, an impact mechanism and an overturning material taking mechanism from left to right, the feeding mechanism comprises a plurality of feeding clamps distributed at equal intervals, and the feeding clamps are arranged on the workbench. The height of the recycling mechanism is lower than the height of the capacitor clamped by the feeding clamp, and the bottom end of the feeding mechanism is connected with a reduction gearbox fixedly installed on the workbench. The problems that according to an existing sorting mode, screening is generally conducted manually, then unqualified capacitors are recycled in a unified mode, time and labor are wasted, and the working efficiency is low are solved.

The application relates to the technical field of power detection, in particular to an intelligent ring network cabinet based on big data, which comprises a cabinet body, a power detection module, a humidity detection module, a temperature detection module, a temperature and humidity adjusting device, a ventilation hole, an external environment detection module, a video detection module, a fireproof device and a central control system, wherein the central control system can determine whether to adjust the temperature and humidity according to the acquired temperature and humidity information in the cabinet body, and can control the opening and closing of the ventilation hole according to the relationship between the ambient temperature and the temperature in the cabinet body. Through the environment detection, the working states of all components in the ring network cabinet are reasonably controlled, so that the temperature and humidity in the ring network cabinet are in the ideal operation range in real time, the accuracy of the power detection data is improved, the maintenance interval length of the equipment is prolonged, and the maintenance cost is reduced.

This invention discloses a method for detecting fake news videos based on retrieval enhancement and prototype alignment techniques. First, the target video is processed to construct its multimodal information. Then, the multimodal information is integrated under a large-scale language model to generate a unified text-centric query, and video retrieval is performed to obtain real and fake video samples semantically related to the target video. Next, a dual prototype alignment mechanism is used to construct prototype representations for real and fake categories using a graph attention network, and a final operation-aware representation is generated through prototype alignment learning. Finally, the final operation-aware representation is integrated with an existing model to complete the detection of the target video content. This method can effectively identify subtle differences between real and fake news videos, achieving enhanced representation learning for video content detection and improving the accuracy and robustness of fake news video detection.

This invention discloses a method and apparatus for detecting deepfake videos based on the consistency of emotion change rates. Applied to the field of data processing technology, this invention is a deepfake video detection scheme that achieves authenticity determination by extracting and comparing the emotion change rates of faces and audio through multiple steps. First, OpenCV is used to read video metadata, and librosa is used to decode audio, unifying the frame rate and sampling rate. Then, OpenCV is used to detect faces and generate continuous time segments. ResNetFaceEmotion is called to extract facial emotion features, and the facial emotion change rate is obtained through sliding windowing, noise reduction, and first-order difference. Simultaneously, EnergyVAD is used to filter and segment the audio, and features are extracted using a preset model to obtain the audio emotion change rate. The intersection of face and audio time segments is taken, and time alignment is achieved using the DTW algorithm and Euclidean distance. Finally, an evaluation model containing bidirectional GRU, self-attention pooling, and a fully connected network is constructed, with the aligned features as input and the detection results as output.