5 results about "Homography" patented technology

This application relates to the field of image processing technology for fly traps, providing an adaptive stitching method and system for fly trap images. The method includes: extracting feature vectors from multiple images using the SIFT algorithm and a ResNet network respectively, and weighting and fusing the two features using adaptive weight coefficients 'a' generated by the ResNet network to obtain a fused feature vector; based on the fused feature vector, using an improved random sampling consensus algorithm to solve the homography matrix between the two images, performing non-uniform random sampling based on similarity during the sampling stage, and using the top K% of the most similar inliers to form a refined inlier set for calculation during the calculation stage; and completing image stitching based on the homography matrix. This application solves the technical problems of difficult feature matching, stitching misalignment, and obvious fusion traces caused by the small size, dense distribution, and high similarity of insect targets in fly trap images, thus improving the robustness, accuracy, and efficiency of image stitching.

This invention discloses a method, system, and medium for human fall detection based on multi-view geometry. The method includes: acquiring intrinsic and extrinsic parameters of multiple fixed cameras and a ground homography matrix; simultaneously acquiring images and obtaining a binary mask of the human body with global identifiers; determining radial lines based on the nadir points of each camera, adaptively selecting the optimal viewing angle camera, extracting foot and head image points, and calculating the horizontal position and vertical height using the homography matrix and geometric relationships; using multi-view ground homography constraints, mapping mask pixels in a reference view to other views for consistency verification, backprojecting verified pixels onto the ground plane to reconstruct the contact area and calculate the area; and fusing at least the temporal changes in height and contact area to determine whether a fall has occurred. This invention can accurately measure key physical quantities related to falls and has advantages such as strong robustness, high accuracy, good interpretability, and controllable hardware costs.

This application discloses a method and system for detecting building exterior wall detachment risks based on deep learning. The method includes: acquiring the original surface image, extracting the facade geometric features and constructing a homography matrix based on the vanishing point for geometric correction, and reconstructing the orthophoto image; enhancing the orthophoto image, extracting the effective detection area, and cropping the detection image; inputting the detection image into a deep learning segmentation model with edge weight constraints, and obtaining the detachment area through semantic segmentation, binarization, and optimization; establishing a mapping relationship based on the actual physical dimensions of the building exterior wall, converting the pixel information of the detachment area into multi-dimensional parameters such as actual physical area, elevation, width, and height; combining the defect density input with a quantitative risk assessment model to calculate a comprehensive risk index, determine the risk level, and output the results. This method can automatically identify and quantitatively assess detachment, eliminate distortion and background interference, and achieve accurate conversion from pixels to multi-dimensional physical space, providing a scientific basis for safety inspections.

The application belongs to the technical field of computer vision and public security, and specifically discloses a composite abnormal behavior recognition method and system in a fire safety and security coupled scene, which comprises the following steps: mapping image coordinates to a top-down physical coordinate system by using homographic transformation to construct a scene geometric semantic model; calculating the physical interference amount of a residual object and a fire door sweeping area, determining that the fire door is blocked when the interference amount exceeds a set threshold, and re-planning a path to generate a dynamic semantic model; extracting the motion trajectory vector of an evacuation individual, and recognizing a reverse behavior by performing a dot product operation on the motion trajectory vector and a safety guide vector in the dynamic semantic model; in an evacuation bottleneck area, identifying a falling target based on shape proportion and height change, and analyzing the motion directionality of surrounding crowds by using a direction consistency cosine value, and generating a stampede warning when the number of crowds converging to the center reaches a set value. The application realizes multi-risk linkage recognition in a unified physical scale.