147 results about "Frame difference" patented technology

The invention discloses a traffic video question and answer method based on inter-frame difference, and the method comprises the steps: innovatively constructing an inter-frame difference extraction module on the basis of an image pre-training model based on the local abrupt change characteristic of a traffic video: effectively capturing the abrupt change characteristic of a moving target through the pixel-level difference calculation of adjacent frames; and designing a gating feature fusion mechanism, dynamically aligning spatial distribution of differential features and visual features on the basis of fusing problem features, and realizing collaborative characterization of static scenes and dynamic changes. In order to enhance cross-modal feature alignment, a hierarchical comparison learning strategy is provided: differential-visual feature comparison is implemented at a bottom layer to enhance local dynamic perception, and text-visual comparison is performed at a high layer to establish global semantic association. Experimental results show that the classification performance is improved on the basis of the SUTD-TrafficQA reference data set.

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 discloses a vehicle monitoring method based on frame difference and deep learning fusion, and relates to the technical field of vehicle monitoring, cameras and environment sensors are deployed in a monitoring area, and videos and multi-source data are acquired by means of vehicle-road cooperation; a self-adaptive frame difference method is used, morphology and optical flow estimation are matched, a threshold value is determined according to the environment, and vehicle features are extracted; constructing a deep convolutional neural network with an attention mechanism, and training a model by using various data in combination with migration and reinforcement learning; fusing the two types of features based on a graph attention network to form high-quality fusion features; a space-time diagram convolutional network is combined with an LSTM to track a vehicle and predict a trajectory, a behavior pattern library is constructed to judge abnormity, and classification analysis is performed in combination with an SVM and a knowledge graph. According to the invention, the frame difference and deep learning are fused, the monitoring accuracy is improved, and the vehicle can be accurately identified and detected; the real-time performance is enhanced, the data is quickly processed, and the environmental influence is reduced; traffic management is assisted, and a safe and efficient traffic environment is created.

The invention relates to the technical field of resolution improvement, in particular to a video definition improvement method and system.According to the video definition improvement method and system, through window accumulation of adjacent frame gray difference and coordinate difference, minimum displacement is selected, mapping is updated, boundary neighborhood weighted correction is carried out, cross-frame dislocation is reduced, and motion area connection is guaranteed; capturing a three-frame gray scale sequence to calculate a difference value, writing a replacement value according to a monotone and amplitude condition, inputting the replacement value and an alignment frame difference into Fourier transform, decomposing amplitude and phase distribution in a residual field in a frequency domain, and performing amplitude and phase consistency elimination on an abnormal block during frame-by-frame superposition to obtain an abnormal block; row difference and column difference absolute values of neighborhood blocks in the residual iteration texture matrix are accumulated into energy, the energy and a center difference are combined with a threshold value to output a mark, and a texture region and an edge region are distinguished; the brightness of the low-frequency position is updated according to the weight mean value, the maximum difference and the direction difference are combined for the high-frequency position, the edge gradient is extracted through the convolutional neural network, a correction value is generated, edge steps and fuzziness are suppressed, and high-frequency details are continuously kept.

The invention discloses a time domain filtering method and device based on video inter-frame difference, equipment and a medium, and relates to the technical field of video coding. The method comprises the following steps: acquiring a to-be-filtered frame and a reference frame corresponding to the to-be-filtered frame, and dividing the to-be-filtered frame and the reference frame to obtain video blocks; determining an inter-frame difference corresponding to each video block in the to-be-filtered frame; the inter-frame difference is the difference between two video blocks at the same position in the frame to be filtered and the reference frame; and determining a filtering weight corresponding to the inter-frame difference according to the difference threshold, and performing time domain filtering on the corresponding video block in the to-be-filtered frame based on the filtering weight. Therefore, the filtering weight is determined by combining the inter-frame difference, and the time domain filtering intensity can be adjusted according to the inter-frame difference, so that the time domain filtering is performed on the to-be-filtered frame by adopting the proper filtering intensity, the time domain filtering capability is improved, and the phenomenon that the code stream is not reduced and then is increased is avoided.

The invention relates to the technical field of robot control, in particular to a robot decision-making method based on deep reinforcement learning, which comprises the following steps: acquiring a change rate data sequence, calculating a frame difference value, screening data frames greater than a threshold value, extracting a mean vector, generating a feature vector set, splicing a ratio of remaining time to distance, constructing a coding matrix and adjusting weight parameters. Screening task groups, mapping and sorting, generating a task batch set, screening adaptive tasks, and generating an execution sequence list. According to the method, adjacent frame difference screening is carried out on multiple change rate sequences in the environment perception data, the mutation section features are extracted, the strategy priority is dynamically adjusted by screening specific inner product results in the strategy evaluation process, the strategy adaptability is improved, the task combination with the minimum conflict and the highest priority is screened, and the strategy evaluation efficiency is improved. And task batches with higher matching degree are screened according to the calculation capability difference value, so that the autonomous response capability and execution reliability of the robot system in a complex task scene are enhanced.

Systems, apparatus, and methods for post-processing video e.g. frame denoising. Noise reduction techniques may be employed to improve the quality of digital video. Frames may be extracted from a video. Synthetic frames may be created using motion data between the extracted frames. Synthetic frames may be masked to exclude pixels from the composite frame. Thresholds used in masking may vary based on the temporal distance of the extracted frame used to create the synthetic frame and the extracted frame. Masking may be based on frame differences between extracted and synthetic frames (e.g., sub-pixel / luminance differences), areas of lower quality motion data (e.g., occlusions), or edge detection in the extracted frames. Synthetic and extracted frames may be composited generating frames having less noise. The composited frame may be based on averaging pixel values across the synthetic and extracted frames. Composited frames may be compiled and encoded into denoised video.

The invention discloses a process pipeline micro-vibration motion amplification detection method based on automatic tracking and deconvolution, and the method comprises the steps: carrying out the automatic frame selection of a vibration abnormal region through an inter-frame difference method, tracking displacement through combining with an LK optical flow method, and analyzing a frequency spectrum, so as to adaptively determine an amplification frequency band; performing vibration signal enhancement by selecting an Euler or phase video motion amplification technology according to the environmental complexity; and further converting the RGB image into an HSV color space, inhibiting motion blur and artifacts by adopting confocal deconvolution, and finally synthesizing a high-definition amplified video. The method has the advantages that automatic recognition of the vibration area and the frequency band is achieved, and the detection efficiency is improved; the image quality is remarkably improved through deconvolution post-processing, and noise interference is avoided; complex industrial environments are compatible, reliable technical support is provided for non-contact pipeline health monitoring, leakage and explosion accidents are effectively prevented, and the service life of facilities is prolonged.

The invention discloses an automobile long-distance speed recognition and tracking method based on target detection, and the method comprises the steps: collecting a vehicle driving video in real time through a fixed camera position and angle, and carrying out the recognition of a vehicle in each frame of image of the video through a pre-trained YOLOv12 target detection model; performing preliminary speed estimation on the automobile by adopting an inter-frame difference method, further constraining a speed change rate, and performing time sequence correction on the speed change rate by adopting Kalman filtering to obtain a corrected speed estimation value; calculating the displacement and the motion deflection angle of the adjacent frames in the transverse and longitudinal directions, and integrating the displacement and the motion deflection angle with the corrected speed estimation value to form a time sequence input feature; and carrying out modeling on a historical track through the trained time sequence deep learning model to realize dynamic speed correction. According to the method, the dependence on physical space calibration is obviously weakened, the perception capability of the model on the motion trend is improved, and the method is suitable for vehicle tracking and speed measurement tasks in long-distance and low-resolution scenes.

The invention relates to a monitoring video stream transmission method and device, equipment and a medium. The method comprises the following steps: acquiring performance parameter data, hardware resource utilization rate data and network condition data of video monitoring equipment and acquired video stream data; calculating a brightness difference in the video stream data by adopting a frame difference method; analyzing the spatial and temporal distribution characteristics of the brightness difference to obtain a motion significance index; determining the priority of video stream transmission according to the motion saliency index; based on the priority, combining the performance parameter data and the hardware resource utilization rate data, and adopting a first preset rule to allocate a video coding strategy for each video monitoring device; and generating a monitoring video stream transmission scheme by adopting a second preset rule according to the video coding strategy, the network condition data and the current hardware resource utilization rate data. According to the invention, the transmission priority and the coding strategy can be dynamically adjusted according to the video content, and the transmission efficiency and the quality stability of the monitoring video stream are improved.

The invention provides a slope rockfall monitoring and early warning method, device and equipment based on dynamic depth estimation and a medium. Relates to the technical field of civil engineering operation maintenance. The method comprises the following steps: S1, acquiring a video sequence image; s2, performing dynamic target detection on the video sequence image by adopting a frame difference method; s3, based on a pre-trained rockfall identification model, identifying rockfall in the target frame; s4, based on a depth-pro monocular image depth estimation model, performing depth estimation on the rockfall in the target frame to obtain a rockfall position distance; s5, performing feature analysis on the rockfall in combination with the depth data to obtain feature parameters; s6, performing tracking and trajectory identification on the rockfall; and S7, performing alarm reminding according to tracking and trajectory recognition results. According to the method, the extracted features are trained to identify the slope rockfall, the position and the size of the rockfall can be identified more accurately, a sharp measurement depth map is rapidly provided, and the dynamic change of the slope rockfall is captured in time.

The invention provides an early-stage enteromorpha identification method, medium and system, and belongs to the technical field of enteromorpha identification. According to the early-stage enteromorpha identification method and system, a dynamic feature fusion module based on inter-frame difference is used for extracting time sequence features of continuous frames, cross-modal information integration is achieved through a double-attention multi-modal feature fusion module, an improved YOLOV8 network model is constructed for target detection, and the identification accuracy of the early-stage enteromorpha is improved. Meanwhile, a liquid neural network model is combined with a multiband ratio method and a floating algae exponential function to carry out spectral feature recognition, enteromorpha components in mixed pixels are processed through a sub-pixel decomposition technology, and finally, multi-dimensional information such as the detection confidence coefficient, the spectrum matching degree, the enteromorpha abundance value and the target number is comprehensively analyzed and detected through a decision fusion algorithm. Accurate recognition and positioning of the early enteromorpha are achieved, and the technical problem that accurate recognition and distinguishing of the early enteromorpha are difficult to achieve at present is solved.

The invention discloses a jamming frame detection method and system for games, and relates to the technical field of computers, and the method comprises the following steps: fusing multi-modal features by using PyTorch, generating time sequence representation through TCN modeling based on the multi-modal features, mapping into jamming probability through a multilayer perceptron structure based on the time sequence representation, and optimizing the jamming probability by using a Focal Loss loss function. And a lagging detection result is generated. The frame difference threshold is dynamically optimized by adopting DQN reinforcement learning, so that the complexity of a game scene can be self-adapted, and the false alarm rate and the omission ratio are remarkably reduced; multi-modal features are fused through a multi-scale SE attention mechanism and PyTorch, and the feature expression ability and the model robustness are effectively improved; tCN time sequence modeling is used to accurately capture a lagging related time sequence mode, the detection precision under unbalanced data is optimized, and the accuracy and practicability of lagging detection in a high-dynamic game scene are enhanced.

The invention discloses a spray head synchronous calibration method based on computer vision. The method comprises the following steps: S1, generating a dense three-dimensional depth point cloud model containing a relative position relation between a spray head array and a substrate; s2, performing inter-frame difference calculation on the binocular difference images of the continuous frames; s3, outputting a current global six-degree-of-freedom pose estimation result of the nozzle array in real time through a joint residual function; s4, constructing a nozzle-level emission time offset calculation model based on the overall six-degree-of-freedom pose estimation result of the nozzle array; s5, generating a corresponding space compensation control instruction based on the three-axis translation deviation and the three-axis rotation deviation contained in the nozzle array global six-degree-of-freedom pose estimation result; s6, through the synergistic effect of the time compensation control in the step S4 and the space compensation control in the step S5, nozzle time synchronous calibration and array space pose synchronous calibration are achieved. According to the invention, global pose information of the nozzle array can be output in real time, and dynamic capture of continuous inter-frame tiny pose changes is realized.

The present invention relates to the field of big data technology, and discloses an urban rail transit emergency handling method and system based on a visual large model, including: collecting visual data, sound data, and equipment operation data at the platforms and carriages of urban rail transit, performing preprocessing and timestamp synchronization; performing dynamic frame difference processing on consecutive video frames of the visual data to generate a two-dimensional motion heat map, and performing spatio-temporal aggregation; constructing a three-dimensional digital model based on the engineering drawings of the platforms and carriages, and establishing a mapping relationship between the two-dimensional motion heat map and the three-dimensional digital model; performing abnormal behavior detection on the mapping results, and performing multi-modal fusion analysis in combination with the sound data and equipment operation data to determine the severity of the abnormal event; formulating an emergency response strategy according to the severity level of the abnormal event, sending control instructions to the rail transit equipment through an industrial protocol interface, and pushing the abnormal event information to the control center, inspection personnel, and passenger terminals.

The invention relates to the technical field of video compression transmission, in particular to a high-reliability network video playing control system, which comprises a data acquisition module used for acquiring network bandwidths at all moments in all transmission time periods and the number of videos being transmitted in all the transmission time periods in the transmission process of network videos; the parameter correction module is used for acquiring the corrected quantization parameter, and comprises the following steps: extracting each connected domain of each frame of image in the network video; obtaining a matched connected domain, a change factor and a contour evaluation value corresponding to each connected domain; calculating inter-frame difference degree and element richness of each frame of image; calculating the network instability, the compression coefficient and the corrected quantization parameter of each transmission time period; and the compression and transmission module is used for carrying out real-time compression and transmission on the network video by combining the corrected quantization parameter with an HEVC algorithm. By dynamically adjusting the quantization parameters, the transmission reliability and the broadcast control fluency of the network video in complex scenes such as a weak network are improved.

The invention discloses a multi-algorithm fusion detection method and device for a dynamic target and a medium, and relates to the technical field of image processing, and the method comprises the steps: collecting continuous video frames of a moving target in a backlight imaging environment; adopting a three-frame difference method to extract dynamic target candidate areas in continuous video frames; on the basis of the structure-enhanced YOLOv8-P2 model, target detection result extraction is carried out on the candidate region under the condition that low-layer feature information is reserved; performing grid slicing on an original video frame in the continuous video frames at a preset overlapping rate, and inputting the original video frame into the RT-DETR model to perform target detection result extraction under background noise suppression; and two target detection results with different emphasis are fused based on spatial position alignment, and coordinates and confidence information of the dynamic target are output. According to the method, advantage complementation is formed through a double-model parallel architecture, and the adaptability of the system in harsh industrial environments such as backlight and dynamic impurities is remarkably improved on the premise that the real-time processing capacity is maintained.

The application discloses a pig farm mouse detection method, system, device and medium based on deep learning, and the method comprises the steps of: frame extraction processing is performed on a video of a to-be-detected area; image preprocessing is performed on an image frame set; a current frame after preprocessing is input into a mouse detection model to learn the appearance of a mouse; the mouse detection model is obtained by using an improved RT-DETR detection model; a pixel with a value of 255 in a binary image obtained by using a frame difference method is taken as a motion pixel, and a corresponding detection result is reserved; a mouse detection result is recorded, and a mouse appearing time length is obtained by weighted summation of the number of mice recorded after each frame detection, so as to calculate a mouse activity index of the to-be-detected area. By adding a frame difference prior branch, the binary image obtained by using the frame difference method is added to the decoding part of the RT-DETR algorithm, and double thresholds are set when the detection result is reserved, so that the model focuses more on the motion area in detection, and the detection success rate of small and fast mice is improved.

The application relates to a high-efficiency distributed video compressed sensing reconstruction system and method, and belongs to the technical field of image processing. ++ The ISTA-Net is introduced into the distributed video compressed sensing system to independently reconstruct and initially reconstruct key frames and non-key frames, and the intra-frame correlation is fully utilized. Through the designed two-stage deep reconstruction network module, the inter-frame correlation is fully utilized to deeply reconstruct the non-key frames. The deep reconstruction network module FMC-Net of the first stage carries out motion compensation to obtain the non-key frames of the preliminary enhanced reconstruction; the deep reconstruction network module FDR-Net of the second stage carries out residual reconstruction by using frame difference information to recover details to obtain the finally reconstructed non-key frames. The method can significantly improve the reconstruction quality of the non-key frames, and high-quality video fast reconstruction is realized.

The invention discloses a video key frame extraction method based on cover frame difference and a related device, and the method comprises the steps: obtaining cover frames of a to-be-processed video clip, calculating the frame feature value of each cover frame in a cover frame set, calculating the similarity between the cover frames based on the frame feature values, and obtaining the similarity between the cover frames; dividing video clips into a plurality of scene groups according to cover inter-frame similarity, on this basis, calculating inter-frame change degrees of the clips in the scene groups, adaptively determining the number of key frames in each group according to the inter-frame change degrees, and finally selecting key frames which can best represent content change in the scene groups. The calculation cost of the key frame extraction process is obviously reduced, and the frame extraction efficiency is improved.

The embodiment of the invention provides a head wearing article detection method and device, equipment, a storage medium and a program product. The method comprises the steps of obtaining video data, performing video stream processing on the video data, generating video stream data, comparing frame differences in a video stream by using an inter-frame difference algorithm, generating frame detection information, judging whether a dynamic object exists in the frame detection information or not, and further judging a wearing article in the video data. According to the embodiment of the invention, the pre-processed video data is enhanced, the wearable article detection model is designed, and the enhanced video data is input into the wearable article detection model to obtain the wearable article detection information, so that the detection effect of the wearable article is improved.

The present disclosure belongs to the technical field of machine vision, and particularly relates to a tunnel abnormal form identification method and system based on machine vision, which comprises the following steps: acquiring a tunnel scene video to be identified; capturing a previous frame image and a current frame image of the acquired video based on a preset time interval; obtaining a frame difference image by processing the captured previous frame image and current frame image through a frame difference method; and analyzing the running characteristics of the tunnel scene video according to the obtained frame difference image and a preset frame difference threshold image, and identifying the abnormal form in the tunnel. The present disclosure can accurately monitor the abnormal burst form in the tunnel, issue a warning when a hazard occurs, and maximize the safety of the construction in the tunnel.

The invention discloses a video action recognition method based on calculation of frame difference information of different frame intervals. The method comprises the following steps: firstly, preprocessing image frames of an original video to obtain a preprocessed video; then, inputting the preprocessed video into an FISNet model based on calculation of inter-frame difference information, and outputting identification features by the model; and the recognition features are input into an action classification module, and the module outputs an action classification result. According to the method, information can be extracted from the frames at different intervals of the video image to adjust the spatial features of the video image, so that the spatial features for enhancing the weight of the key space part are obtained, and more accurate video image action recognition is obtained.

The present invention relates to the technical field of termite monitoring data processing, and specifically to an intelligent analysis method and system for monitoring data of a termite visual monitoring device, including: designing a termite visual monitoring device for monitoring termite activities; collecting videos through the termite visual monitoring device, calculating background differences using the frame difference method, dynamically updating the background template, and extracting termite activity frames; combining with a termite detection model to obtain the coordinates and quantity of activity bounding boxes, calculating the moving direction and speed of termites, generating termite data and uploading it to the cloud; obtaining termite data through the cloud, calculating the movement index, activity index, frequency index and activity degree, and at the same time combining environmental data, using an activity prediction model to predict the future termite activity trend. Through real-time monitoring and activity prediction, the first and second alarm messages are respectively sent out to achieve a comprehensive analysis and accurate early warning of termite activities, thereby improving the monitoring efficiency.

The present invention relates to a method for detecting the working conditions of pumping stations and gate stations based on current-video bidirectional analysis, comprising: obtaining a video stream of the working status of pumping station and gate station equipment, detecting frame differences in the video stream using a frame difference method, accumulating the frame differences in the video stream within a first set time, and obtaining the motion intensity within the first set time; calculating the probability of video anomalies using the motion intensity within the first set time; collecting the current values ​​of the pumping station and gate station equipment within a second set time, analyzing them, and obtaining the probability of a continuously high current, a continuously low current, a frequent current fluctuation, and a sudden current change; then combining the probabilities after current analysis to obtain the total probability of current anomalies; and fusing the video anomaly probability and the total probability of current anomalies to obtain the detection results of the working conditions of the pumping station and gate station equipment. This method has a higher accuracy rate than the detection results obtained by a single feature in the prior art, and the acquisition of video streams and current values ​​is less difficult and easy to implement, and is less affected by the external environment.

The application discloses a kind of real-time optical flow detection systems on chip based on frame difference type sensor, including frame counter, buffer array, memory array and on-chip processor, frame counter, for generating the time label of event;Buffer array, for temporarily storing the event output of sensor array;Memory array, for temporarily storing the event data of corresponding pixel position in sensor array, flag bit data and the time label of the event;On-chip processor, for completing the calculation and control of detection system;The application has the advantages that: real-time accurately carries out the estimation of optical flow direction, high precision.

The invention relates to the technical field of video data, and discloses a compression extraction and processing method for key frames in video data, which comprises the steps of input and preprocessing, inter-frame difference score calculation, optical flow motion score calculation, fusion scoring, time sequence smoothing and local extremum screening, redundancy suppression, and key frame compression and index reconstruction. Through the steps of inter-frame difference analysis, optical flow motion field calculation, adaptive fusion scoring, time sequence smooth constraint, redundancy suppression and feature compression encoding, adaptive extraction and compression processing are performed on frames with significant changes or semantic importance in a video sequence, and low-layer change features are obtained through pixel layer frame difference calculation. Capturing local and global motion features through optical flow vector field modeling; constructing a criticality scoring model, carrying out weighted fusion on multi-channel features, carrying out smoothing and extreme value detection on a time dimension, and selecting a most representative video key frame; compression and index structure reconstruction are completed, and structured storage and efficient restoration of video data are achieved.

An embodiment of the present invention provides a method and device for detecting the transitional state of a monitoring video lens and a method for segmenting a lens. First, calculate the similarity of adjacent video frames in a video file in sequence. After normalizing the similarity, determine the inter-frame difference value, as well as the rising and falling segments of the inter-frame difference value. Then, compare the inter-frame difference value with a mutation threshold to obtain the index of the mutation transition in the video. Finally, according to specific comparison rules, compare the first and last inter-frame difference values of the candidate gradual transition segments with the gradual change threshold respectively. Among all the candidate gradual transition segments, screen out the true lens gradual transition state and obtain its index, completing the process of detecting the transitional state of the monitoring video lens and segmenting the lens. This solution solves the problem that the maximum number of frames in the gradual transition state in the dual-threshold video lens detection algorithm needs to be determined manually, reduces the usage limitations of the algorithm and the requirements for prior experience, and improves the applicability and flexibility of the algorithm.

The invention provides a face counterfeit video recognition method and device, a storage medium and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: obtaining a to-be-recognized face video; performing frame difference processing on each adjacent video frame in the face video to obtain a plurality of frame difference pictures; dividing each frame difference picture into image blocks in N area ranges; forming an image block sequence by the image blocks in the same area range in each frame difference picture, and obtaining N image block sequences; processing the N image block sequences by using a pre-constructed recognition model to obtain a recognition result of the face video; the recognition result represents whether the face video is a forged video or not. By applying the method provided by the embodiment of the invention, the recognition accuracy of the face counterfeit video can be improved.

The invention discloses a GPU (Graphics Processing Unit) parallel hierarchical motion detection method and a GPU parallel hierarchical motion detection system for edge computing. The method comprises the following steps: acquiring multi-channel video stream data, and copying the processed multi-channel video stream data to a GPU (Graphics Processing Unit) according to whether an edge device supports DMA (Direct Memory Access) to match a corresponding data processing strategy; on the GPU, performing global motion filtering on the processed grayscale images of the current frame and the previous frame of video data, obtaining a difference graph through parallel vectorization inter-frame difference, performing tree reduction summation, and calculating to obtain the number of global motion pixels; and judging whether the number of the global motion pixels exceeds a preset threshold value, if so, calculating a motion area of the global motion pixels and positioning a motion target, and otherwise, directly processing a grayscale image of the next frame of video data. According to the invention, the actual demand of large-scale and low-cost edge deployment is met, and the balance among performance, power consumption, cost and compatibility is achieved.