112results about How to "Conform to visual characteristics" patented technology

The invention discloses an automobile A pillar perspective vehicle-mounted display device which reflects dynamic changes of driver sight angles. Cameras are arranged both inside an automobile and outside the automobile, the cameras outside the automobile are used for collecting surrounding sight information of a road ahead, and the cameras inside the automobile are used for obtaining driver vision information; according to driver sight concerning angles, images concerned by a driver are cut from images obtained by the cameras outside the automobile; flexible liquid crystal display screens are arranged on the inner side of an A pillar in an attaching mode according to the radian of the A pillar to display images; the images displayed on the flexible liquid crystal display screens inside the automobile are changed in real time according to driver sight angle change information collected by the cameras inside the automobile; the angle and the size of a driving scene image, collected by the cameras outside the automobile, at the position of a blind region are changed in real time, and an A pillar display system can be adjusted in a self-adaption mode according to sight needing regions detected through sight tracking. Therefore, external road traffic environment information which is more complete, more accurate and good in driver acceptability is provided for the driver, and the safety and the convenience of automobile driving are improved.

The invention discloses an enhancement method for a low-illumination image. The enhancement method comprises the steps that the low-illumination image to be processed is acquired and then the low-illumination image to be processed is transferred to an HSV color space from an RGB color space, and a chroma component, a saturation component and a brightness component are acquired; the brightness component is decomposed into a reflection component and an irradiation component by adopting an alternating minimization method based on a Retinex algorithm; the irradiation component and the reflection component are respectively enhanced and then synthesized into an enhanced brightness component; the saturation component is self-adaptively adjusted and then an enhanced saturation component is obtained; the chroma component, the enhanced brightness component and the enhanced saturation component are synthesized into a new HSV image; and the obtained new HSV image is converted into an RGB image, and an enhanced image is obtained through white balance processing. Definition of the low-illumination image can be greatly enhanced, and details are enabled to be reproduced so that the method is high in applicability and high in robustness and can be widely applied to the field of image processing.

The invention discloses an objective evaluation method of no-reference video quality weighted by key frame image quality. The method comprises the steps of: firstly, preliminarily selecting a key frame according to a movement intensity method weighed by a human eye interest, then dividing the preliminarily selected key frame into a scene switching type key frame and a non-scene switching type key frame by correlation analysis; evaluating image quality of the non-scene switching type key frame, subdividing the non-scene switching type key frame into a content change type key frame and a quality change type key frame according to an evaluation result; finally carrying out weighted summation calculation on single frame quality by using a duration factor and a deterioration frequency factor of the quality change type key frame, and obtaining the quality evaluation result of an entire video sequence. According to the objective evaluation method, the defect that then existing evaluating technology cannot be coincided with an actual subjective feeling; computation complexity can be reduced on the premise of ensuring the evaluating performance; the objective evaluation method is suitable for automatic evaluation of various video applications of an existing network, simple in operating steps and easy to integrate.

The invention provides an infrared image and visible image fusion method based on guide filtering. The method comprises the steps of: firstly, expanding the guide filtering to multiple scales, and performing multi-scale decomposition on an infrared image and a visible image by using the multi-scale guide filtering, so as to obtain a low-frequency subband and a high-frequency subband; performing nonsubsampled directional filtering on the high-frequency subband, so as to obtain a directional subband coefficient; applying different fusion rules to the directional subband coefficient and a low-frequency subband coefficient, so as to obtain the corresponding directional subband coefficient and the low-frequency subband coefficient after fusion, and finally, performing directional filtering reconstruction and multi-scale reverse transformation based on the guide filtering, so as to obtain a final fusion image. According to a fusion result obtained by adopting the method, the edge and thermal radiation characteristics of an infrared image object are preferably kept, scene details of the visible image can be preferably reserved, and the information content of a fusion image is increased, so that the edge and details of the fusion image are enriched.

The invention discloses a video area-of-interest exacting method based on depth; the invention has the advantages of associatively utilizing texture video frame and depth video frame corresponding to the texture video frame, both of the texture video frame and the depth video frame are synchronous on time; the method comprises the steps as follows: firstly obtaining motion region of the texture video frame and binary mask image of contour texture, and each depth layer of the depth video frame and binary mask image of depth discontinuous regions, then obtaining the binary mask image of each depth object layer of each depth layer, and the binary mask image of contour feature region of each depth layer, then taking the binary mask image of contour feature region of each depth layer as feature, for scanning the binary mask image of each depth object layer, obtaining the area-of-interest in the binary mask image of each depth object layer, finally stacking the area-of-interest in the binary mask image of each depth object layer to obtain the area-of-interest of the texture video frame; the area-of-interest obtained by the method conforms to the semantic feature and has low calculating complexity and high precision.

The invention discloses a compressive sensing theory-based satellite remote sensing image fusion method. The method comprises the following steps of: vectoring a full-color image with high spatial resolution and a multi-spectral image with low spatial resolution; constructing a sparsely represented over-complete atom library of an image block with high spatial resolution; establishing a model from the multi-spectral image with high spatial resolution to the full-color image with high spatial resolution and the multi-spectral image with low spatial resolution according to an imaging principle of each land observation satellite; solving a compressive sensing problem of sparse signal recovery by using a base tracking algorithm to obtain sparse representation of the multi-spectral color image with high spatial resolution in an over-complete dictionary; and multiplying the sparse representation by the preset over-complete dictionary to obtain the vector representation of the multi-spectral color image block with high spatial resolution and converting the vector representation into the image block to obtain a fusion result. By introducing the compressive sensing theory into the image fusion technology, the image quality after fusion can be obviously improved, and ideal fusion effect is achieved.

The invention discloses an image nonlinearity enhancement method based on histogram subsection transformation. The method comprises the following steps of: S1, calculating probability density distribution of image gray levels; S2, calculating accumulated probability density functions of the gray levels; S3, dividing the gray levels of the images into three parts, wherein the first part of gray level range is between 0 and 85, the second part of gray level range is between 86 and 170, and the third part of gray level range is between 171 and 255; respectively calculating gray level transformation functions of the three parts of gray levels according to the accumulated probability density functions, namely proposing sectional gray level transformation functions; and S4, transforming the gray levels of the original images by using the sectional gray level transformation functions to acquire the distribution of new gray levels. By the method, the definition of the image is further improved, the excessive enhancement of the image is avoided, and the image vision effect meets the requirement of an eye vision system.

The invention discloses a low-illumination image enhancement method and system, and belongs to the technical field of image processing. The method comprises the steps of converting an input original image into an HIS space from an RGB space, keeping the saturability and the hue component of the image unchanged, enhancing the brightness component, converting the image with the brightness enhanced into the RGB space from the HIS space and outputting the image. According to the low-illumination image enhancement method and system, the image brightness is improved, extremely-dark areas (the areas close to black) are prevented from being changed into bright-colored areas after being enhanced, and the image adapts to the human visual characteristics better. Meanwhile, the complexity is low, the calculation amount is small, high-definition images can be processed in real time, and the performance requirement for hardware can be lowered.

The invention discloses a light-emitting control method, apparatus and system for an LED (light emitting diode) lamp. The light-emitting control method comprises the steps of receiving a control message; analyzing the control message; determining a control mode and a control target value of the LED lamp, wherein the control mode of the LED lamp comprises a lightening mode, a luminance regulation mode, an extinguishing mode and a toning mode, and the control target value comprises a three-color target duty ratio; determining a light-emitting gradual change parameter corresponding to the control mode of the LED lamp; calculating a gradual change PWM duty ratio corresponding to the light-emitting gradual change parameter in the toning process of the LED lamp, wherein the value range of the light-emitting gradual change parameter is from 0.005 to 0.05; outputting a PWM (pulse width modulation) signal to an actuator, wherein the duty ratio of the PWM signal is the gradual change PWM duty ratio; and controlling the LED lamp to give out light in a gradual change manner, wherein the frequency of the PWM signal is greater than 100 hertz. According to the light-emitting control method, apparatus and system for the LED lamp, the relatively soft and uniform light emission of the LED lamp can be realized, the emitted lights can better accord with visual characteristics of human body, and the user experience is improved.

The invention relates to a Retinex-theory-based low-illumination low-altitude remote sensing image enhancing method, mainly comprising the following steps of: transforming a low-illumination low-altitude remote sensing image from a red, green, blue (RGB) color space into a hue, saturation, intensity (HSI) color space; adjusting a luminance component I and a saturation component S on the premise of guaranteeing that a hue component H is not changed, so that the processed image accords with visual characteristics of eyes of a human body and a color distortion phenomenon is avoided; transforming the enhanced image from the HIS color space into the RGB color space; and performing convolution operation by using a laplace operator and an original image to obtain an edge image; and superposing the enhanced image and the edge image to obtain the finally enhanced image. By virtue of the method, the luminance and the contrast of the low-illumination low-altitude remote sensing image are improved, the color distortion phenomenon is avoided, and the low-illumination low-altitude remote sensing image has rich edge information and accords with the visual characteristics of the eyes of the human body.

The invention belongs to the technical field of image fusion and provides a method and a device for evaluating image fusion quality. The method comprises the following steps of: obtaining source images and a fused image of the source images; segmenting the source images by adopting a fuzzy clustering method to obtain segmented images, and uniting the segmented images into an overall segmentation map; obtaining visual variance saliency maps of the source images; computing weight maps according to the visual variance saliency maps, and computing saliency coefficients of all areas of the source images and the fused image according to the visual variance saliency maps and the overall segmentation map; computing weighted structure similarities of the fused image and the source images in the areas according to the overall segmentation map, the weight maps and the saliency coefficients; summing up the weighted structure similarities of all areas to obtain an evaluation index for the quality of the fused image. Compared with the existing EFQI (edge-dependent fusion quality index) and the traditional objective evaluation indexes, the evaluation index obtained by adopting the method provided by the invention has higher consistency with objective evaluation.

The invention discloses a stereoscopic video quality evaluation method based on a binocular minimum discernible distortion model, which includes: firstly building a binocular stereoscopic brightness relation model, and researching respective pixel brightness of a left viewpoint video and a right viewpoint video and the brightness relation of the pixel brightness of the left viewpoint video and the right viewpoint video when they integrate to form a stereoscopic video ; building a stereoscopic image just noticeable difference (JND) model according to background brightness and texture masking, and building a stereoscopic video JND model according to a visual threshold value masked by video interframe; obtaining a visual threshold value of a function based on space-time contrast sensitivity through the function based on space-time contrast sensitivity and relative parameters of a display device; obtaining a binocular JND model by combining the stereoscopic video JND model and the visual threshold value of the function based on space-time contrast sensitivity; and finally building a binocular perception peak signal-to-noise ratio based on stereoscopic video quality for evaluating stereoscopic video quality. The method is based on the binocular JND model, leads evaluation of stereoscopic video quality to be identical with video quality subjectively perceived by human eyes, and correctly reflects human eye visual stereoscopic video perception quality.

The invention discloses a moving target detection method based on a particle filtering visual attention model. First of all, a particle filtering bidirectional fusion attention model is constructed according to the Bayesian estimation principle; next, on the basis of the particle filtering bidirectional fusion attention model, movement attention and target color attention serve as B-U attention input and T-D attention input respectively, the particle distribution state is changed by calculating particle weights, an attention saliency map is formed, and finally the position of a moving target is determined. According to the method, time attention and space attention are fused, so that the movement attention is calculated more accurately; bottom-to-top attention and top-to-bottom attention are fused, so that the forming process of human visual attention is simulated simply and effectively; with respect to a complex global movement scene, the effectiveness and accuracy of moving target detection are improved.

The invention provides an image segmentation quality evaluating method based on an ROC Graph. The image segmentation quality evaluating method includes the following steps: firstly, dividing pixels in a to-be-evaluated segmentation image into four types by comparing with a reference segmentation image; secondly, leading in pixel space information, performing distance transform on pixels in the reference image, and calculating distance from each pixel to a target boundary; thirdly, giving a weight to a pixel at a corresponding position in the segmentation image according to distance coefficient of each pixel in the reference image; fourthly, utilizing the weights to calculate a weighted true positive rate wTPR and a weighted false positive rate wFPR, and depicting a point (wFPR, wTPR) in the ROC Graph; fifthly, according to distance from the point (wFPR, wTPR) to a point (0,1), scoring the segmentation image, wherein the higher the score is, the better the segmentation effect of the to-be-evaluated image is. By the image segmentation quality evaluating method, quality of the segmentation image can be evaluated effectively, and the evaluation result is more reasonable and more visual and consistent with subjective evaluation result of human vision.

The invention particularly relates to a generative image steganography method based on an adversarial network, which comprises the following steps: a rule table is established, and secret informationto be hidden is converted into coordinate information according to the rule table; According to a two-point one-line principle, a ciphertext generator is established, and ciphertext coordinate point information is obtained; The sender randomly selects the ciphertext coordinate point information, replaces the label information with the ciphertext coordinate point information, inputs the ciphertextcoordinate point information into the generator, and generates a transfer image of a specified category; After receiving the transmission image, the receiver inputs the transmission image into a discriminator to obtain a category label, and then ciphertext information can be extracted; Decrypting is performed to obtain hidden coordinate information according to a two-point one-line principle; Andhidden secret information is obtained by comparing with the rule table, and information extraction is realized. According to the method, steganalysis detection based on statistics can be fundamentallyresisted, the safety of information transmission is improved, and meanwhile the steganalysis resisting capability is greatly enhanced.

The invention discloses a vehicle safety auxiliary video image processing method. Images acquired by a vehicle-mounted video device are processed. The method comprises the image enhancement, noise reduction and strong light inhibition method, the road line detection method and the obstacle monitoring method. According to the processing method of the invention, the improved Retinex algorithm is adopted to perform local enhancement on the images, and at the same time, the dynamic histogram mapping algorithm is adopted to perform overall image enhancement, the two enhancement results are combined in the weight form to obtain the final enhanced images, effects, such as image enhancement, noise elimination, color sharpening, etc., can be realized, the image contrast and definition can be improved to obtain images which are close to the original images and conform to human visual characteristics, and characteristics of road line detection and obstacle distance measurement can be realized.

The invention discloses a multi-focus noise image fusion method based on a cycle spinning-Contourlet domain hidden Markov tree model (CS-CHMT) and an improved dual-channel pulse-coupled neural network (IDPCNN). First, two multi-focus images containing a certain level of Gauss white noise are de-noised by use of the CS-CHMT model, and on the basis, a fusion strategy is designed by use of the IDPCNN to obtain a final fused image. According to the invention, by making use of the directional sensitivity of Contourlet transform height and the advantages of anisotropy, performing image de-noising by use of a hidden Markov tree (HMT) model, and introducing a cycle spinning technology to effectively suppress the pseudo Gibbs effect of the images near singular points, the PSNR value of de-noised images is improved. Compared with the traditional multi-focus image fusion method, the improved IDPCNN fusion method can effectively preserve more detailed information characterizing image features, greatly improve the quality of fused images and further improve the visual effect, and has real-time performance.

The invention provides a multi-modality image fusion method based on a region and human eye contrast sensitivity characteristic, comprising the following steps of: (1) respectively utilizing a non-subsampled Contourlet transform NSCT to carry out multi-scale decomposition on a source image to be fused to obtain each stage of sub-band coefficient of the source image; (2) respectively formulating a fusion rule of a low-frequency sub-band coefficient and each stage of high-frequency sub-band coefficient according to a human eye vision contrast function LCSF, a human eye vision absolute contrast sensitivity function ACSF, a feeling brightness contrast function FBCS and a local average gradient sensitivity function LGSF to obtain each grade of the sub-band coefficient of a fused image; and (3) carrying out NSCT inverted conversion on the fused coefficient and reconstructing to obtain the fused image. The multi-modality image fusion method disclosed by the invention meets vision properties of a human eye and improves the quality of the fused image to a great extent; and the multi-modality image fusion method has robustness, is suitable for fusing a plurality of types of image sources of infrared and visible light images, multi-focusing images, remote images and the like, and has a wide application prospect.

The invention discloses a color image color constant method based on a retina vision mechanism, which belongs to the technical field of image processing, and relates to a color image constant technology, in particular to a color image color constant method based on a retina vision mechanism. The invention is based on the current latest optic nerve electrophysiology research achievement to simulatethe retina vision mechanism of human eyes and establish an approaching receptive field single antagonist model to realize the constant color of a deviation color image. The color image color constantmethod based on a retina vision mechanism confirms that the proposed arithmetic is more effective than the current color constant arithmetic through the image validation of more than three hundred different scenes and illumination in the international universal color constant test database; besides, the color image color constant method based on a retina vision mechanism not only can realize theconstant color of the deviation colorful image, but also has the function of reinforcing the image color, thus being more suitable for the visual characteristic of the human eye.

The invention discloses a capsule endoscope image retrieval method based on wavelet transformation. The method comprises the following steps: constructing an image database including standard pathological images and images to be retrieved; transforming the color spaces of the standard pathological images and the images to be retrieved in the image database to the HSI (hue, saturation, intensity) form; extracting the low-frequency waveband information from each HIS component of the processed image, and calculating the feature similarity between the processed standard pathological images and each image to be retrieved by use of Euclidean distance; and selecting the image to be retrieved with low feature similarity as the suspected pathological image. The method is used for processing the capsule endoscope images, and can retrieve the abnormal images from the images to be retrieved by comparing the capsule endoscope images with the pathological images, thereby reducing the labor intensity of film reading doctors and improving the diagnosis efficiency.

The invention discloses an image enhancing method based on the visual characteristics of human eyes, for example, the human eyes has poor resolution ratio on grayscale when the grayscale of an image is very high or very low, the human eyes has high resolution ratio when the grayscale is moderate. The image enhancing method considers the visual characteristics of the human eyes in the image processing process and improves the defects of the prior image enhancing method. Neighborhood information plays an important role in the image smoothing and denoising process, thus adjacent pixel points canbe used for enhancing processing efficiency in the image processing process. The image enhancing method can smooth and denoise the image and enables the enhanced image to more accord with the visual characteristics of the human eyes.

The invention discloses a video quality evaluation method based on three-dimensional wavelet transform. The three-dimensional wavelet transform is applied to video quality evaluation, secondary three-dimensional wavelet transform is performed on each frame group in a video, and description of time domain information in the frame groups is finished through decomposition of a video sequence on a timeline, so that the problem of difficulty in describing the time domain information of the video is solved at a certain degree, the objective quality evaluation accuracy of the video is effectively increased, and the correlation between an objective evaluation result and human eye subjective perception quality is effectively improved; and specific to time domain correlation existing among the frame groups, the quality of each frame group is weighted through motion intensity and brightness characteristic, so that the method disclosed by the invention can well comfort to human eye visual characteristics.

The invention provides a tongue image environment adaptive color reproduction method of traditional Chinese medicine and belongs to the field of medical image processing. The method comprises the steps of at first, shooting a special color plate image under the same image collection condition and measuring standard colorimetric values of color lumps in the special color plate by colorimeter; establishing a collection process color characterization model through color plate image data in a computer to realize conversion between equipment color space and standard chroma space; and then calculating displayed image chroma tristimulus values by a visual adaption model according to measured obtained image collection environment and displayed environmental parameter which is determined through combination with a background adaption parameter determined by tongue image structure prior knowledge. The method is similar to collection process characterization in that through display of a group of color lump on a displayer and measurement of standard chroma value of displayed color lump, establishment of the collection process color characterization model, and calculation of the display drive value corresponding to chroma tristimulus value. With the tongue image environment adaptive color reproduction method of traditional Chinese medicine , adaptive true color reproduction of tongue images is finally achieved.

The invention discloses a high-dynamic-range image information hiding method. The method is characterized by, in secret information embedding, obtaining three channel values of each pixel point in anoriginal high-dynamic-range image; then, according to each channel value of each pixel point and corresponding 5 index bits, determining an embedding significance bit of information to be embedded ineach channel value of each pixel point; embedding the information in each channel value of each pixel point to obtain a high-dynamic-range image embedded with secret information; in secret informationextraction, obtaining three channel values of each pixel point in the high-dynamic-range image embedded with the secret information; then, obtaining embedding position of information in each channelvalue embedded with the information of each pixel point; and extracting the information from each channel value embedded with the information of each pixel point to obtain a secret information sequence. The advantages are that the method can embed the secret information into the high-dynamic-range image in an OpenEXR format; and when the embedding rate is 30 bits per pixel, the image embedded withthe secret information can still keep high fidelity.

The invention discloses a stereoscopic video bitrate control method based on binocular just-noticeable distortion. Bitrate control is performed through a viewpoint layer, a group of picture layer, a frame layer and a macro-block layer. In the viewpoint layer, a first group of picture is pre-coded to obtain bitrate allocation weight of left and right viewpoints; in the group of picture layer, the target number of bits is allocated for each group of picture according to the remaining number of bits in the viewpoints, a frame rate, the length of the groups of picture and a buffer occupancy rate, and an encoding quantization parameter of a key frame in each group of picture is determined; in the frame layer, the target number of bits is allocated for each frame except the key frame according to the remaining number of bits in the groups of picture and the buffer occupancy rate; and in the macro-block layer, the target number of bits allocated to each macro-block in each frame except the key frame is calculated, and an encoding quantization parameter for each macroblock is determined. The above bitrate control method allows bitrate control results to be more consistent with human visual characteristics under definitive bitrate conditions and can be used for obtaining good subjective quality.

The invention relates to a method for detecting characteristics of edge and angular point of color pictures. The technical proposal of the method is characterized by comprising the steps that: calculating the spatial gradient of each color channel of the color pictures, projecting the spatial gradient onto the color space which is insensitive to the illuminance information of shadow, light and shade and spectrum; generating an unchanged-illuminance color tensor by using unchanged-illuminance differential coefficient and own Cartesian inner product; and carrying out characteristic decomposition to obtain the partial orientation corresponding to the maximum characteristic value; then a filtering kernel of a symmetrical orientation tensor is controlled and generated by the partial orientation information, convolution filtering based on the filtering kernel of the symmetrical orientation tensor of the unchanged-illuminance color tensor is carries out to obtain a new color tensor; the characteristic value decomposition of the new color tensor is carried out to obtain a characteristic value and a characteristic vector and derive a characteristic measurement; detecting the characteristic of the edge and angular point. The detection of the characteristic of the edge and angular point is carried out by utilizing the characteristic of the sensor, thereby providing the designed characteristic detecting apparatus with visual characteristic better conforming to the eyes of human beings and restraining stronger noises.

The invention aims to provide an improved non-classical receptive field-based bionic contour detection method. The method comprises the following steps of A, inputting a to-be-detected image subjectedto grayscale processing; B, presetting a Gabor filtering function containing multiple direction parameters, performing classical receptive field stimulus response on each pixel point in the to-be-detected image, wherein a corresponding direction serves as an optimal direction of the pixel point; C, building an inhibitory kernel function by utilizing a log function, building a distance weight function through the inhibitory kernel function, and performing convolution on the classical receptive field stimulus response of the pixel point and the distance weight function of the pixel point to obtain inhibitory response of the pixel point; and D, subtracting a product of the inhibitory response of the pixel point and preset inhibitory intensity from the classical receptive field stimulus response of each pixel point, and performing calculation to obtain a final contour value of the pixel point. The detection method overcomes the defects of the prior art and has the characteristics of meeting spatial characteristics of visual receptive field and having a better detection effect.

The invention relates to a multi-band image synchronous fusion and enhancement method. Multi-band image fusion and image super-resolution are synchronously realized; the invention especially relates to a multi-band image synchronous fusion and enhancement method based on improved WGAN-GP. The method comprises the following steps: designing and constructing a generative adversarial network, whereinthe network structure is composed of a conceptual feature extraction (coding) network, a feature combination network and a decoding network, a generative model is obtained through dynamic balance training of a generator and a discriminator, and a fusion enhancement result is obtained. According to the invention, the end-to-end synchronous fusion and enhanced neural network of multi-band images isrealized, so that a high-quality fusion result is fused from a low-resolution source image.

The invention relates to a fusion method for multi-band images, in particular to a self-supervised learning fusion method for multi-band images based on a multi-discriminator generative adversarial network, which comprises the following steps of: designing and constructing a generative adversarial network which consists of a generator and a plurality of discriminators, and a label image is a multi-band source image; wherein a generator network structure is composed of a feature enhancement module and a feature fusion module which are conceived, acquiring a generation model through dynamic balance training of a generator and a discriminator to acquire a multi-band image fusion result. A multi-band image end-to-end self-supervision fusion neural network is realized, a result has better definition and information amount, the detail information is richer, and the method better conforms to the visual characteristics of human eyes.

The invention provides an image transmission method, equipment, movable platform, monitoring equipment and system, and the method comprises the steps: a first image shot by first shooting equipment carried by the movable platform and a second image shot by second shooting equipment are acquired (S101); image processing is performed on the first image and the second image, and after that, image data is obtained (S102); and the image data after image processing is sent to the monitoring equipment (S103). The image data is obtained through acquiring two images shot by two shooting equipment respectively carried by the movable platform and performing image processing on the two images, the image data is sent to the monitoring equipment, two images restored by the monitoring equipment accordingto the image data, the two images are respectively displayed through two display equipment, lack of parallax information or depth information is avoided, the distance of an object in a monitoring screen can be accurately judged, and precision of remote monitoring is improved.