114 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 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 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 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 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 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 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.

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.

The invention provides a video stream playing and analyzing system and a working method thereof, the system performs video stream media playing and video analysis, and the system comprises a multi-video stream playing module, a multi-channel playing module, a video stream comparison module, a code stream size detection module and a video stream broadcasting module; the method comprises the following steps: S1, acquiring chip channel information; s2, acquiring information of a video stream; basic information of the code stream; s3, according to functions, performing S4 playing, S5 detection and S6 comparison respectively; s4, multi-video playing: playing each video stream; s5, code stream size detection: counting code stream information of 256 frames; respectively displaying the code stream information of each frame and the statistical information of the first 256 frames; s6, video stream comparison: respectively acquiring video stream information of the video A to be detected and the video B to be compared; respectively extracting and displaying images in the two videos; obtaining a video C with a mark after frame difference; judging whether the display end displays A or B; after determining a display video, fusing the video with the C; and displaying the final video.

The invention relates to the technical field of non-contact physiological signal detection, and discloses a remote physiological signal detection method and system based on state space and dual-path interaction. The method comprises the steps of obtaining a sample set; frame difference preprocessing is carried out on the original face video, and time sequence features are extracted; performing time sequence dependence modeling on the extracted time sequence features through a state space model and a state space duality mechanism; performing frequency domain feature enhancement on the time sequence features through a frequency domain feed-forward network; respectively inputting the time sequence characteristics after the frequency domain characteristics are enhanced into a self-attention path and a cross attention path, then fusing the time sequence characteristics after the two paths are processed, and taking the fused data as a predicted rPPG signal; performing model training by minimizing the difference between the predicted rPPG signal and the real rPPG signal; and inputting a face video of a to-be-detected person into the trained prediction model, and outputting a corresponding prediction rPPG signal. The detection method provided by the invention has good accuracy and robustness.

The application discloses a forklift illegal use monitoring method, medium and equipment based on images, and relates to the technical field of forklift monitoring. Two images captured by a camera at an interval of a first preset time length are acquired; frame difference binarization processing is performed on the two images to obtain a frame difference image; a target minimum circumscribed rectangle is determined for a connected region of the frame difference image; whether a width-length ratio of the target minimum circumscribed rectangle is less than a first preset threshold value is judged for each target minimum circumscribed rectangle; if the width-length ratio is less than the first preset threshold value, it is determined that the target minimum circumscribed rectangle corresponds to a forklift illegal use. Frame difference binarization processing is performed on two images captured by a camera at an interval of a first preset time length to obtain a frame difference image, a target minimum circumscribed rectangle is determined for a connected region in the frame difference image, and whether a forklift illegal use behavior exists is determined according to whether a width-length ratio of the target minimum circumscribed rectangle is less than a first preset threshold value, so that consumption of network resources can be reduced, and labor cost can be lowered.

The invention relates to the technical field of computer vision and industrial safety monitoring, and discloses a video target detection method and system based on a coupling frame difference yolov8 model, and the method comprises the steps: 1, collecting a video stream in real time, extracting an RGB image of a current frame, and converting the RGB image into a gray-scale map; meanwhile, a historical frame grey-scale map cache queue with a fixed length is constructed and maintained and is used for storing grey-scale information of recent frames; step 2, calculating a frame difference between a current frame grey-scale map and a historical frame grey-scale map at an interval of 10 frames in a buffer queue, introducing a space-time coupling causal attention mechanism, and generating a self-adaptive attention weight single-channel frame difference image; 3, taking the single-channel frame difference image as a motion feature channel, and splicing the single-channel frame difference image with the RGB three-channel image of the current frame in the channel dimension to construct a four-channel fusion data tensor; and step 4, training a yolov8 detection model by using the four-channel fusion data, so that the yolov8 detection model can cooperatively utilize static appearance and dynamic motion clues, and a detection result is output.

The invention provides a valve opening and closing operation detection method and an applicable system and a readable medium thereof, and relates to the technical field of computer visual recognition. The detection method comprises the following steps: acquiring a plurality of continuous image frames containing the switch component in a preset time period; frame difference values corresponding to a plurality of continuous moments associated with the plurality of continuous image frames are obtained through calculation according to the pixels of the plurality of continuous image frames; a plurality of image frames before the valve is opened and closed for the first time are selected from the plurality of continuous image frames to serve as static frames; obtaining a starting time threshold value and an ending time threshold value corresponding to the same opening and closing operation according to the frame difference values corresponding to the plurality of static frames; and for a plurality of continuous moments corresponding to each opening and closing operation, taking the continuous moment corresponding to the first frame difference value greater than the starting moment threshold value as the starting moment of the same opening and closing operation, and taking the continuous moment corresponding to the first frame difference value less than the ending moment threshold value after the starting moment as the ending moment of the same opening and closing operation.

The application provides an image detection method and device and a computer readable storage medium. A current image in a video stream is obtained, and a reference image is obtained. The current image is input into a rain removal model to obtain a processed image processed by the rain removal model. A first frame difference image of the current image and the reference image is obtained, and a second frame difference image of the processed image and the reference image is obtained. Difference information of the first frame difference image and the second frame difference image is obtained, and it is determined whether the difference information meets a preset condition. In the manner, whether it is a rainy day can be accurately determined through the difference information of the current image and the current image processed by the rain removal, other factors in the image background can be excluded, the accuracy is improved, misjudgment is prevented, and robustness is improved.

This application discloses an IoT video transmission method, system, device, and medium. The method acquires first IoT video stream data at an edge terminal. The first IoT video stream data includes several first video frames. Inter-frame content analysis is performed on the first IoT video stream data to obtain inter-frame difference data for each first video frame. Based on all inter-frame difference data, dynamic frame extraction is performed on the first IoT video stream data to obtain second IoT video stream data. The second IoT video stream data is sent to a server, whereby the server performs frame interpolation processing on the second IoT video stream data, obtaining third IoT video stream data, which is then sent to the user terminal. This method can effectively reduce IoT video transmission bandwidth while improving the integrity and clarity of video transmission, thereby enhancing the user's video viewing experience. This application relates to the field of IoT technology.

The application provides an image occlusion intelligent detection method and device, which comprises the following steps: based on a video sequence, a frame difference method is used to determine a first continuous N1 frame area without pixel change as an initial background area; the video sequence after the first continuous N1 frame is divided into multiple continuous processing periods; in each processing period, the following steps are executed: based on the video frame in the current processing period, a pixel change area is calculated and the effective background area of the current processing period is updated according to the pixel change area, which is used as the effective background area of the next processing period; a fusion Bhattacharyya distance between the effective background area and a reference frame is calculated; the mean value and the standard deviation of the fusion Bhattacharyya distance corresponding to the processing period of the first N2 video frames in the video sequence are calculated, and an adaptive threshold is calculated according to the mean value and the standard deviation; each fusion Bhattacharyya distance is compared with the adaptive threshold in sequence, and according to the comparison result, it is determined whether the video frame has occlusion. The method and device of the application have low false positive rate, the threshold is self-adjusted, and the determination is accurate.