479 results about "Color mapping" patented technology

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention discloses a video image color correction method and system based on artificial intelligence, and relates to the technical field of color correction, and the method comprises the steps: collecting video data, carrying out the image size normalization and denoising processing, and obtaining the preprocessed video frame sequence data; and extracting reflectivity data and illumination data by using a Retinex-Net deep learning model, calculating an illumination adjustment coefficient, carrying out pixel correction, and carrying out color temperature correction on the pixel data of the video frame subjected to pixel correction according to the illumination adjustment coefficient. According to the method, the preliminary color mapping matrix is generated through the color histogram matching method, the color distribution of the image can be optimized, the image is enabled to better conform to the color features of the target reference frame, the color temperature adjustment is performed on the image according to the illumination adjustment coefficient and the illumination threshold, the cold and warm tones of the video are enabled to be accurately adjusted, and the image quality is improved. Through successively carrying out pixel correction, color correction and local area correction, a stable color adjustment frame is provided, so that the video styles are consistent.

The invention relates to the technical field of medical cosmetology and computer vision, in particular to a 3D skin measurement method based on high-precision measurement of a face concave-convex degree region, and the method comprises the following steps: arranging a structured light camera to collect a face RGB image and depth data, and carrying out the filtering and denoising to generate a smooth point cloud; detecting an RGB image based on FaceMesh to obtain face key points, and constructing a multi-region segmentation algorithm; segmenting an RGB local area and converting the RGB local area into point cloud data; iDW interpolation is carried out to generate a dense point cloud, and a two-dimensional contour line is extracted through a Marking Square algorithm; and mapping the color of the matched color card to an image area to generate a contour map. According to the method, the face recognition model is constructed through the FaceMesh architecture, accurate positioning of multiple face types is adapted, two-dimensional contour mapping is adopted to three-dimensional contour mapping, data are intercepted, local processing is combined, the operation load is reduced by 60%, and efficiency is remarkably improved.

The invention discloses a harbor district accident risk visualization analysis method based on a network topology structure, and the method comprises the steps: taking a key facility and an important region as network nodes in a harbor district, determining a connection relation to obtain a network topology structure, analyzing historical cases to obtain a determined risk factor set, obtaining an uncertain factor set through edge calculation, and carrying out the calculation of the uncertain factor set. The method comprises the following steps: constructing an accident tree to carry out quantitative evaluation on network nodes and connection risks, constructing a risk evolution model, extracting an accident cause chain and calculating a risk influence range, visualizing a network topology structure, carrying out color mapping on the accident cause chain and the risk influence range, and then adding the accident cause chain and the risk influence range to obtain a three-dimensional topology view. According to the method, the network topology structure visualization analysis of the accident risk of the harbor district can be realized, the risk is comprehensively evaluated, the subsequent management decision of the harbor district is optimized, and meanwhile, the method has relatively good interpretability.

The invention discloses an industrial image change anomaly detection method and system based on artificial intelligence, and relates to the technical field of image recognition, and the method comprises the steps: collecting a dual-light-source industrial image, employing frequency domain saliency to guide fusion, and carrying out visual enhancement processing through color mapping and edge enhancement; inputting the enhanced image into a CNN convolutional network to generate a multi-scale feature map, extracting a multi-scale high-pass residual image through two-dimensional fast Fourier transform and a high-pass filtering template, and splicing all scales and coding to generate a token sequence through local attention guide fusion; constructing a self-induction visual model, and performing feature reconstruction on the token sequence to generate a reconstructed feature map; and calculating and reconstructing an error scoring graph by adopting double error indexes, sampling to obtain an abnormal smooth graph, and segmenting an abnormal region based on the abnormal smooth graph. And finally, a dual anomaly detection system of image-level judgment and region-level identification is constructed.

The invention relates to the technical field of tire production, in particular to a method for identifying abnormal wear of a tire through color difference of an automobile tire tread, which comprises the following steps: determining scanning parameters; correcting scanning parameters; obtaining a point cloud picture; quantitative parameters are selected, and a wear state model is trained based on historical detection data; establishing a mapping relation; according to the detection data of the to-be-detected automobile tire and the wear state model, obtaining a quantization parameter value of the automobile tire; generating a quantization parameter point cloud picture; verifying whether the quantization parameter value is accurate or not according to the spectrogram of the reflected light and the quantization parameter point cloud atlas; and generating a visual quantization parameter point cloud picture. According to the method, scanning parameters are dynamically corrected according to the actual perimeter to ensure the accuracy of point cloud data, automatic calculation of quantization parameters is realized in combination with a machine learning model, and finally, abstract wear data are converted into a visual image through color mapping, so that the wear condition is directly obtained by observing colors, and the accuracy of the wear condition is improved. And the detection efficiency of abnormal wear of the tire is improved.

A method of generating a color mapping table for correcting color of a display panel, including: establishing a sampling point space, according to first color coordinates of a plurality of sampling points in a first color space; and performing steps of a preset processing on each of the plurality of sampling points, to obtain target color coordinates of the sampling point in the first color space, and generating the color mapping table according to the first color coordinates and the target color coordinates of the plurality of sampling points, where the steps of the preset processing on each of the plurality of sampling point include: taking the sampling point as a source sampling point, and determining a target sampling point corresponding to the source sampling point; and processing the target sampling point, to obtain the target color coordinates.

The invention provides a method and a system for reconstructing a hyperspectrum from an RGB image. The method comprises the following steps: acquiring a three-channel RGB image; performing embedding and channel expansion on the RGB image by using a convolutional neural network to obtain an initial feature map; inputting the initial feature map into an encoder, respectively extracting channel dimension features and space dimension features by utilizing VisionTransform and VisionMama in the initial feature map, fusing the channel dimension features and the space dimension features, and respectively inputting the channel dimension features and the space dimension features into jump connection and separable convolution to obtain feature maps of different levels; inputting the feature map into a decoder to obtain a final feature map; mapping the final feature map by using a convolutional neural network, and adding the final feature map with an initial feature map before the final feature map is input into an encoder to obtain a hyperspectral image in a compressed form; and splitting the hyperspectral image in the compressed form according to the number of channels, and then performing color mapping to finally obtain a reconstructed hyperspectral image. According to the method, the precision and generalization performance of reconstructing the hyperspectrum from the RGB image can be improved.

The present disclosure describes a system and a method for object detection and counter measures. A signal reflected by an object in an environment is conditioned to improve various parameters such as signal to noise ratio, spectral resolution, color mapping, or the like. A determination whether the object is unmanned aerial vehicle is based on an output of a trained AI model. The trained AI model classifies the detected object into a category based on the conditioned signal. Additionally, a jammer and spoofer are orchestrated based on determination that the object is an unmanned aerial vehicle. A control of the object is achieved based on the orchestration to perform counter measures such as jamming and spoofing.

The invention provides a line lamp color transformation control method and device, a lamp and a medium. The method comprises the following steps: acquiring ambient light intensity, color distribution, a user position and an action type in real time; a color mapping model trained by a neural network is utilized to convert the color parameters into color parameters; generating a color transformation instruction based on color mixing and a brightness compensation algorithm according to the color parameters; an instruction is sent to a driving circuit to control color conversion, and meanwhile, according to feedback of a temperature sensor in the line lamp, when the temperature exceeds a threshold value, the switching frequency and the brightness value are reduced, and a transition mode is changed; the normal temperature gradually recovers, and safety and effect are guaranteed. The color of the line lamp is accurately adjusted according to the environment and user behaviors, human eye perception is considered in a color mixing and brightness compensation algorithm, instructions are dynamically adjusted according to the temperature, the lamp is protected, sensors of the lamp are matched, control is achieved through a computer program, and the lighting experience is improved.

The invention relates to a photovoltaic monocrystalline silicon material resistivity uniformity evaluation method, and belongs to the technical field of photovoltaic material detection. The method comprises the following steps: evaluating all-form resistivity and doping uniformity of a photovoltaic single crystal silicon rod, a single crystal silicon wafer and a single crystal silicon square wafer; carrying out standardized point distribution on the monocrystalline silicon wafer by using a concentric circular ring and a small circle, and calculating in-plane uniformity by using a triple integral derivation equation; the monocrystalline silicon square pieces are distributed in the form of grid small squares, and in-plane uniformity is calculated by means of a triple integral derivation equation; and for the axial direction of the silicon rod, the uniformity is quantified by utilizing range and variance models through the resistivity of the head, middle and tail wafers. Meanwhile, a continuous grid is generated through interpolation, and a 3D color mapping curved surface diagram with plane projection is drawn to visually present distribution. According to axial and in-plane uniformity results, silicon wafers are classified and graded, the doping uniformity of monocrystalline silicon is accurately reflected, and a support is provided for quality control, process optimization and silicon wafer sorting of photovoltaic monocrystalline silicon.

The invention relates to the technical field of medical education, in particular to a virtual patient skin care teaching system with enhanced pathological features and a method thereof, the system comprises pathological feature generation, parameter control, physical modeling, intelligent evaluation and a multi-mode feedback module, and can edit and simulate various skin pathological features in real time, such as pressure sores and diabetic feet, so that the system can be applied to the field of medical education. The visual display of the lesion degree is realized through a five-segment color mapping system, the system also simulates percolate reflection and cuticle spalling, the sense of reality is enhanced, the intelligent assessment module provides risk assessment and healing prediction according to physiological and morphological parameters, the multi-modal feedback module integrates visual sense, tactile sense and user behavior feedback, the interactive experience is improved, and the system is suitable for popularization and application. And the adaptive optimization module dynamically adjusts system parameters according to user feedback and learning effects, and provides personalized teaching. Compared with a traditional method, the method is higher in parameter control precision, supports personalized teaching, and is of great significance to medical education.

The invention discloses an intelligent perception analysis system for the real-time participation degree and cognitive state of a mathematics classroom. The intelligent perception analysis system comprises a multi-source data acquisition module, a fusion analysis engine module, a real-time feedback module and an offline optimization module. The method has the beneficial effects that a dynamic participation index is innovatively designed, and an attention attenuation resetting mechanism is introduced; the real-time feedback module generates a cognitive thermodynamic diagram of HSL color mapping to position group obstacle points, and triggers self-adaptive question pushing; and the off-line optimization module dynamically updates the edge weight of the knowledge graph through error co-occurrence analysis, and early warns a cognitive confusion relationship without textbook association. According to the method, the limitation of single-mode perception is broken through, the problem solving step quality visual diagnosis and the cross-cycle cognitive impairment prediction are realized, a teaching closed loop of'multi-source perception-hierarchical quantification-real-time intervention-knowledge evolution 'is formed, and the mathematical classroom cognitive state analysis precision and the teaching intervention timeliness are remarkably improved.

The invention discloses a zero sample object instance segmentation system and method in a robot environment, and belongs to the technical field of robot visual perception, and the method comprises the steps: firstly carrying out the Vibriis color mapping preprocessing of a depth image, and then generating an initial object mask candidate through an SAM (Segment Anything Model) model; meanwhile, a pre-trained Vision Transform is used as a feature description model of a scene to process the image, an attention map of the last layer is extracted, and a feature weighting mechanism is constructed based on the information entropy of the attention map; a similarity matrix of the image blocks and the background blocks is calculated to remove non-object masks, a K-Medoids clustering algorithm is adopted for each remaining object mask candidate to obtain representative sampling points, and finally the points serve as prompt information to be input into an SAM model to obtain an accurate object instance segmentation result. According to the method, the zero sample generalization ability of the visual basic model is fully utilized, accurate segmentation of the unseen object can be realized without additional training, and the method has good universality and practical value.

The invention belongs to the technical field of computer vision and image processing, and particularly discloses a video defogging method and system based on enhanced alignment, and the method comprises the steps: carrying out the color mapping of a plurality of foggy images through a color lookup table, and obtaining a plurality of enhanced images; based on the enhanced image corresponding to the foggy target frame and the enhanced image corresponding to each foggy reference frame, performing space-time optical flow estimation to obtain a three-dimensional optical flow; performing image transformation on the foggy reference frame based on the three-dimensional optical flow to obtain an alignment frame; and based on the alignment frame, the foggy target frame and the enhanced image corresponding to the foggy target frame, performing splicing and feature fusion to obtain a defogged image corresponding to the foggy target frame. According to the method, details of the foggy image are enhanced, a more accurate alignment result is obtained, more inter-frame information is provided through accurate alignment frames, the network can be effectively helped to learn a better defogging capability, the utilization capability of the deep neural network on the inter-frame information can be improved, and a better video defogging effect is achieved.

The invention provides a fatigue test method, system and equipment based on texture change of carbon fibers and a medium, and belongs to the technical field of material tests.The method comprises the steps that a carbon fiber composite material is selected as a test sample; applying a fatigue load to the test sample according to a designed test condition, and collecting a surface image of the test sample; the method comprises the following steps of: preprocessing an acquired image, clustering according to the gray level and texture similarity of pixels to obtain a plurality of clustering subareas, and marking different clustering areas by using different colors to complete color mapping; extracting texture feature parameters from the image after color mapping, and performing PCA dimension reduction to obtain key texture feature parameters; and comparing the key texture feature parameters of the image under different test conditions, identifying texture feature changes, and completing fatigue performance evaluation. According to the method, image processing and texture feature analysis are combined, so that non-destructive testing of the CFRP fatigue performance is realized, and the testing accuracy and efficiency are improved.

A three-dimensional temperature gradient field visualization method comprises the following steps: after analyzing a part model and storing a point-surface relationship and temperature information, calculating a data point temperature gradient and carrying out point cloud, grid and entity model visualization; after creating temperature field color mapping and calculating a temperature field contour line, representing a temperature gradient field based on a point icon, representing a temperature gradient field based on a streamline and representing a temperature gradient field based on texture; through design of a data visualization core algorithm and model building, visualization of three-dimensional data of various vector fields for an unstructured grid model is realized.

The invention discloses a reservoir four-relation chart processing method, device and equipment and a medium, and relates to the field of oil well exploration, and the method comprises the steps: building a logging information parameter processing interpretation model, and determining parameters in an oil saturation model; based on the neutron-density intersection chart, calculating the distances from the logging response parameters to different lithologic curves as lithologic indication parameters; drawing a reservoir four-relation chart by taking the porosity of the stratum as an abscissa, the resistivity of the stratum as an ordinate and the lithology indication parameters as color mapping, drawing different water saturation scale marks in the reservoir four-relation chart, and in addition, taking the water saturation or the movable hydrocarbon saturation in the reservoir four-relation chart as a bubble radius; and projecting the logging response parameters of various stratum types and the explaining stratum parameter points into the reservoir four-relation chart, and establishing a logging explaining standard. According to the method, the dynamic change and internal relation among the four properties of the reservoir can be comprehensively and accurately displayed.

The invention relates to the technical field of underwater image enhancement processing, in particular to a pyramid network-based underwater image dynamic enhancement method and application, and the method comprises the steps: decomposing an input underwater image into layered image groups; using a deterministic color mapping network to convert the low-frequency layer input image into a low-frequency layer output image; using a double convolution attention module to obtain enhanced input features; using a packet expansion feedforward network to obtain a pixel-by-pixel mask; performing operation on the enhanced input features and step-by-step pixel masks to obtain high-frequency components; and reconstructing the low-frequency layer output image and the high-frequency component by using a Laplacian pyramid network to obtain an enhanced output underwater image. In conclusion, the underwater image can be dynamically enhanced, the adaptability is high, the method is suitable for various water bodies, color distortion can be effectively corrected, noise introduction is restrained, more image details can be reserved, and the underwater image has better visibility and higher recognition degree.

The invention provides an ultra-high-definition long-distance full-color imaging method under extremely low illumination and a module thereof, and relates to the technical field of image processing, and the method comprises the steps: carrying out the short-exposure shooting of the same scene, synchronously obtaining a plurality of visible light frames and a plurality of infrared frames, and carrying out the image registration; performing brightness enhancement on the plurality of corresponding visible light frames based on the plurality of infrared frames, and synthesizing the plurality of visible light frames after brightness enhancement by using an adaptive weighted fusion algorithm to generate a long exposure equivalent single-frame image; performing noise reduction processing on the long exposure equivalent single-frame image and the plurality of infrared frames; inputting the long-exposure equivalent single-frame image after noise reduction processing and the plurality of infrared frames into a pre-trained color derivation model for color reconstruction, and generating a color information matrix for constructing a full-color image; and based on the color information matrix, performing color mapping and coloring on the long-exposure equivalent single-frame image to generate a final full-color image.

The invention relates to the technical field of computer graphics and deep learning, in particular to a simulation bait automatic coloring method and system based on a 3D model. The method comprises the following steps: acquiring an uncolored 3D model and a reference image, and generating standard data through analysis verification, curvature grid division and image compliance detection; performing color conversion, texture enhancement and multi-scale downsampling on the compliant image to construct an image pyramid; performing multi-level feature extraction and adversarial training optimization based on a pre-trained convolutional neural network and a generative adversarial network, and generating an enhanced color texture map; performing UV expansion, color mapping and normal mapping fusion in combination with the model topology, and constructing an intermediate model with physical rendering attributes; and batch color consistency verification is realized through color histogram comparison, adaptive threshold segmentation and iteration parameter adjustment, and a standard model group is generated. According to the invention, efficient and highly realistic automatic coloring of the simulated bait is realized, and color consistency and rendering quality in batch production are guaranteed.

The invention discloses a method for optimizing the texture quality of a generative 3D model based on a texture expansion graph, and relates to the field of computer vision. Rendering the 3D model to obtain a multi-modal image set under multiple view angles; obtaining a training data set and training a multi-view generation model; generating an RGB image set by using a multi-view generation model; high-quality RGB images under the six visual angles are back-projected to the surface of the 3D model, colors of 3D points are mapped to a UV space through UV mapping, and a multi-visual-angle texture map and a mask map of the UV space under the six visual angles are obtained; fusing the multi-view texture maps to obtain a rough texture map; and optimizing the rough texture map through the image restoration model to obtain a high-quality complete texture expansion image. Through the multi-view generation model and the UV space image restoration technology, the texture quality of the generation type 3D model is optimized, and the problems of artifacts and distortion caused by the fact that the image content is inconsistent with the three-dimensional model geometry when high-quality multi-views are generated based on images are effectively solved.

Aspects of hair simulation, hair classification, and networks therefor including aspects to train such networks are provided. A classifier model alleviate the impact of human bias where the modeling of the real label distribution and annotators' biases are separated by incorporating annotator confusion matrices into a baseline model. The model was trained using a consistency-based semi-supervised learning framework. With the use of only 1000 labeled data, the final classifier model achieved a classification accuracy that was 20% higher than a human professional annotator. The model is useful as a color classifier to train generative models for hair color translation. There is provided a generative model for hair simulation (e.g. via VTO) that is guided during training by a hair classifier model. Further provided is a color mapping network to process an input image and target hair color for the generative model to define the hair simulation.

The invention discloses a visual characterization method for carbon emission of a sewage treatment plant, belongs to the field of carbon emission monitoring, and solves the problem that the fluctuation and trend of carbon emission data cannot be analyzed by an existing method. The method comprises the following steps: checking carbon emission intensity; preprocessing the carbon emission intensity data; defining a color mapping function according to the numerical range of the carbon emission, and mapping the numerical value to a color space; based on the color mapping function, creating a color bar for representing a corresponding relationship between the data value and the color; according to the color mapping function and the color bar, converting the carbon emission data at each time point into a corresponding color, and drawing the color on the heat map; and optimizing and outputting the image. According to the method, the calorific value map technology is introduced, so that the visualization effect of carbon emission intensity data is remarkably improved, the change of the carbon emission intensity in a time sequence is visually displayed, complex data becomes clear at a glance, and the peak value and the fluctuation trend of carbon emission can be quickly captured.

The invention relates to an image processing model training method, an image processing method and related equipment, and the method comprises the steps: respectively inputting a sample image and a sample reference image into a feature extraction network and a color mapping table generation network of a to-be-trained image processing model, obtaining a sample feature map, and generating a color mapping table; inputting the sample feature map into a light scene recognition network of the model to obtain a light scene recognition result, and performing fusion on the generated color mapping table in a light scene dimension based on the light scene recognition result to obtain a target color mapping table; and performing color mapping processing on a sample image based on the target color mapping table to obtain a target processing image, determining target loss based on the target processing image, the sample image and the sample reference image, and training a to-be-trained image processing model by using the target loss to obtain an image processing model. The image processing effect is improved, skin color editing of any expected effect can be achieved, and the image processing effect is improved.

The invention belongs to the technical field of building single or multi-view-angle to 3D modeling reconstruction, and provides a 3D reconstruction method based on a real estate building cluster point cloud to MESH neural network. Comprising the steps of original point cloud data acquisition, noise removal, grid filtering, normal vector optimization, density uniformity processing, continuous multilayer 3D convolutional neural network processing, color mapping, texture mapping, color consistency optimization and texture distortion optimization. Through uniform sampling, noise removal and grid filtering, the point cloud data volume is reduced, and the quality of the point cloud data is improved; through the continuous multi-layer 3D convolutional neural network, the local geometric pattern of the building is automatically learned from the data and the features of the building are extracted layer by layer, so that the precision of the three-dimensional grid model is improved; through color consistency optimization and texture distortion optimization, the color and texture mapping effect is optimized, and refined reconstruction of the building is realized.

The invention discloses an automatic texture color mapping method for large-scale point cloud data. The automatic texture color mapping method comprises the following steps: firstly, realizing geometric alignment of geographic texture data and point cloud data; then realizing release of intra-core buffer by means of a mass point cloud space segmentation and retrieval management technology, and segmenting the mass point cloud into a plurality of regional point cloud blocks; carrying out filtering processing on the regional point cloud blocks to filter out discrete flying spots; secondly, considering correction constraints of different texture source information to realize seamless generation of geographic texture data; secondly, efficient point cloud preliminary color assignment is achieved by means of the spatial relation between geometric mapping and local aggregation of regional point cloud blocks and geographic texture data; secondly, eliminating the texture information (color) of the wrong coloring point cloud by using the self-shielding judgment result of the point cloud; and finally, realizing spatial merging of regional point cloud block data sets by using an efficient out-of-core file merging technology. According to the method, the dependence on hardware equipment (such as a large memory and a high graphics card) is avoided, and the block processing of massive point clouds is efficiently realized.

Embodiments of the present disclosure provide techniques for colorizing visual content using artificial intelligence models. An example method generally includes receiving an image and an input prompt specifying a colorization to apply to the image. Based on an encoded version of the image and a textual description of the image input into a machine learning model, one or more color maps associated with the specified colorization to apply to the image are generated. A colorized version of the image is generated by a generative artificial intelligence model based on combining a grayscale version of the image and the one or more color maps, and the colorized version of the image is output.

The invention discloses a digital twinning-based hydrodynamic model display method, system and device and a storage medium, and the method comprises the steps: obtaining a simulation result of a hydrodynamic model from local storage or network data service, and storing the simulation result in a data structure of a digital twinning system; a water body three-dimensional model is generated through the grid Shp file, vertex flow field and water level data are calculated based on a model simulation result, and after normalization, the vertex flow field and water level data are mapped to a color channel and vertex height of the water body three-dimensional model; a water surface dynamic effect is enhanced by adopting a water surface ripple normal mapping and flow velocity color mapping, and PostProcessVolume is created to simulate interface refraction and underwater visual effects of the water surface and air. The three-dimensional flow field and water level change of the hydrodynamic model can be displayed, visualization of underwater simulation results is supported, and the sense of reality of the flow effect is improved; and in combination with network data service and a local file access mode, the data loading and updating speed is increased, and the method is suitable for various data access environments.

The invention discloses a digital signal real-time processing system based on multi-core distributed processing, which relates to the technical field of digital signal processing and comprises a data receiving module, a parallel processing module, a process scheduling module and a terminal interaction module. Identifying a coding protocol of the input video digital signal, receiving the input video digital signal through an external interface of a preset multi-communication protocol, and processing the input video digital signal to obtain a to-be-analyzed video frame sequence; performing color space conversion processing on a to-be-analyzed video frame sequence to obtain a grayscale image sequence, a hue component and a saturation component, performing calculation through two-dimensional fast Fourier transform and a cascade filter bank to obtain enhanced video frame grayscale image data, and performing pseudo-color mapping to obtain an enhanced video frame image; and according to the current frame image and the previous frame image of the to-be-analyzed video frame sequence, calculating to obtain a current frame motion feature map and a moving target positioning mask, so that digital signal processing is efficiently completed at the front end to obtain an enhanced image and target mask positioning.