The invention discloses a human flow statistics method based on RGB and D information fusion, which comprises the following steps: step 1, collecting an image sample containing human head information;2, marking that head area of the RGB sample, cutting the positive and negative sample, regularizing the size of the cut sample image and attaching corresponding sample labels to different samples; 3,extracting HOG features of all RGB sample image, and inputting that extracted HOG features and the corresponding sample tag into an SVM classifier for training; 4, detecting the head area of the RGBimage in the newly inputted video frame by using the trained SVM classifier; 5, detecting the depth information of Depth image of the newly inputted video frame; 6, fusing that detection results to obtain a final detection target frame; 7, tracking the target object to obtain the moving trajectory of the target object, and counting the human flow information.

Statistical method of pedestrian flow based on RGB and D information fusion

The embodiment of the invention discloses a point cloud encoding and decoding method and an encoding and decoding device, relates to the technical field of encoding and decoding, and is used for providing a technical scheme for effectively encoding and decoding point cloud data. The point cloud coding method can comprise the following steps: obtaining a normal coordinate axis of a patch to be coded in a point cloud to be coded and description information of a minimum enclosing rectangle corresponding to the patch to be coded; wherein the description information of the normal coordinate axis ofthe patch to be coded and the minimum enclosing rectangle corresponding to the patch to be coded is used for determining a tangent coordinate axis of the patch to be coded and an auxiliary tangent coordinate axis of the patch to be coded; compiling the syntax elements into a code stream; wherein the syntax element is used for indicating a tangent coordinate axis of the patch to be coded and an auxiliary tangent coordinate axis of the patch to be coded; wherein the syntax element comprises an index of a normal coordinate axis of the patch to be coded and information used for indicating description information of a minimum bounding rectangle corresponding to the patch to be coded.

Point cloud encoding and decoding method and encoder-decoder

The embodiment of the invention provides a visual positioning method and device. The visual positioning method comprises the steps of acquiring a to-be-positioned target scene image; detecting the target scene image based on the trained character area detection model to obtain a character area of the target scene image; on the basis of the trained character content recognition model, recognizing the content of the character area and acquiring character content information corresponding to the character area;and performing matching according to the text content information and the database image information to obtain positioning information corresponding to the text content information. According to the embodiment of the invention, the text information contained in the target scene image isdetected and identified; According to the technical scheme, the corresponding text content information is obtained after the target scene image is obtained, and then the text content information is matched with the image information in the image database, so that the positioning information corresponding to the target scene image is obtained, the difficulty of visual positioning is reduced, and the positioning accuracy and robustness are improved.

visual positioning method and device

The embodiment of the invention discloses a panorama abutted seam detection method and device. The method comprises the steps: obtaining a preset number of local images and initial panorama depth images photographed by panorama photographing equipment; splicing the preset number of local images into a panorama based on the initial panorama depth images; extracting a line graph used for representing preset lines from the panorama; determining splicing lines in the line graph, wherein the splicing lines are corresponding to a boundary line between two adjacent local graphs in the panorama; determining the proportion of the number of pixels with different colors to the total number of pixels on the two sides of the splicing lines in each row of pixels on the two sides of the splicing lines; and in response to determining that the specific gravity meets a first preset condition, outputting information used for representing that the abutted seam in the panorama is too large. According to the embodiment of the invention, effective detection on the abutted seam in the panorama containing the three-dimensional information is realized, and the splicing effect of the panorama an be improvedaccording to the abutted seam detection result.

Panorama abutted seam detection method and device, readable storage medium and electronic equipment

The invention discloses a corner detection-based rectangular box three-dimensional size measurement method and a storage medium. According to the method, color map information acquired by an RGB camera is utilized to find a position where the end point of a rectangular box may exist on an image; depth map information and an internal reference thereof are obtained by effectively utilizing a depth camera; and through combining the color map information and the depth map information, the three-dimensional sixe information of the rectangular box is efficiently and accurately obtained. The method and the storage medium are relatively wide in application range and high in universality.

Corner detection-based rectangular box three-dimensional size measurement method and storage medium

The invention discloses an RGB-D feature matching-based 3D point cloud registration and fusion method. The method mainly comprises the following steps of: projecting scene surface 3D point cloud dataof different viewpoints to RGB-D images to carry out processing, so as to realize dimensionality reduction calculation from three-dimensional point cloud to two-dimensional point cloud; extracting feature points of the RGB-D images, establishing feature descriptions of the RGB-D images, and matching the RGB-D images of the different viewpoints; solving a coordinate and a depth transformation matrix, and registering and fusing the RGB-D images; and converting the fused RGB-D images into 3D point cloud data. Through the method provided by the invention, calculation of three-dimensional feature extraction and matching can be simplified, and the efficiency of three-dimensional point cloud registration and fusion can be improved. The method provided by the invention can be applied to scene reconstruction of 3D large fields of view and 3D full views.

RGB-D feature matching-based 3D point cloud registration and fusion method

The invention discloses a three-dimensional scene characteristic expression and high-precision matching method based on an RGB-D image. The method comprises the steps of detecting and extracting three-dimensional characteristic points of the RGB-D image by means of a perspective projection model and a scale space theory; by means of rotation invariance of a circle, selecting four concentric circleareas with one characteristic point as a circle center as characteristic description areas required for the characteristic point; respectively calculating the gradient modulus and direction of a pixel point in each circular ring area and an innermost central circle area, and establishing a direction histogram, wherein the circular ring area is the annular area between two adjacent centric circles; establishing an annular descriptor by means of the direction histogram, performing characteristic description on each characteristic point, generating characteristic vectors, and matching the characteristic points according to the Euclidean distance between the characteristic vectors. Through the method supplied by the invention, three-dimensional characteristic expression and matching is performed on the RGB-D image, thereby preventing main direction distribution, reducing the number-of-dimensions of the characteristic vector, reducing calculation amount and saving time for subsequent characteristic matching, and realizing high real-time performance in characteristic matching.

Three-dimensional scene characteristic expression and high-precision matching method based on RGB-D image

The invention discloses a focus scanning and computational imaging method based on fuzzy function characterization, the method comprises the following steps: establishing a focus scanning data acquisition model; according to a fuzzy function theory, characterizing a focus scanning data acquisition model and solving a one-dimensional fuzzy function, a two-dimensional fuzzy function, a one-dimensional optical transfer function and a two-dimensional optical transfer function of the focus scanning data acquisition model; according to the optical transfer functions and a relation between the optical transfer functions and a point spread function, analyzing three-dimensional space invariance of the focus scanning data acquisition model; and performing three-dimensional deconvolution computational imaging based on the three-dimensional invariance of the two-dimensional optical transfer function of the focus scanning data acquisition model. With the method provided by the invention, the focusscanning data acquisition model can be designed and computational imaging can be executed, and an analysis means based on the fuzzy function is provided for other depth of field development imaging systems.

Focus scanning and computational imaging method based on fuzzy function characterization

The invention discloses an RGB-D-based scene three-dimensional feature point efficient extraction method. The method comprises the following steps of step 101, using a perspective projection model tofigure out a dimension-reduction calculation method for a RGB-D image fidelity-based three-dimensional geometric structure and obtain a parametric representation of a scene in a camera coordinate system; step 102, starting from a diffusion equation, and establishing a RGB-D scale space for detecting the three-dimensional feature points of a RGB-D image based on the finite difference and scale space theory; step 103, carrying out extremum detection in the RGB-D scale space and acquiring the positions of feature points; step 104, based on the sub-pixel interpolation method, accurately positioning feature points and screening out low-contrast-ratio and edge response points. In this way, the feature matching stability and the anti-noise capability are enhanced. By the adoption of the method, the three-dimensional feature extraction of images can be carried out. The method provides a large amount of stable image features for follow-up image registration, image splicing, target tracking, target recognition and the like.

RGB-D-based scene three-dimensional feature point efficient extraction method

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RGB-D feature matching-based 3D point cloud registration and fusion method

Three-dimensional scene characteristic expression and high-precision matching method based on RGB-D image

Focus scanning and computational imaging method based on fuzzy function characterization

RGB-D-based scene three-dimensional feature point efficient extraction method