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Is the CovarianceMatrix in Get3ZoneCovarianceMatrix should be 2D or 3D? #20
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Hi, which function are you referring to? compute3ZoneCovarianceMatrix or computeMomentOfInertiaTensorNormalized |
refer to the function compute3ZoneCovarianceMatrix. Sorry, i make some error before, the elements used are as fellows: |
So I guess you refer to this block?
As the original paper said |
Hi, Yan, @yzrobot, thank you for your reply first. |
Then, I take a test, in the following block: computeProjectedPlane(pc, pca.getEigenVectors(), 2, centroid, main_plane);
compute3ZoneCovarianceMatrix(main_plane, pca.getMean(), f.partial_covariance_2d); The cov-matrix from the upper zone from "main_plane" is defined as main_plane_upper_cov_matrix. computeProjectedPlane(pc, pca.getEigenVectors(), 2, centroid, main_plane);
pcl::PCA<pcl::PointXYZI> main_plane_pca;
pcl::PointCloud<pcl::PointXYZI>::Ptr main_plane_projected(new pcl::PointCloud<pcl::PointXYZI>);
main_plane_pca.setInputCloud(main_plane);
main_plane_pca.project(*main_plane, *main_plane_projected);
pcl::PCA<pcl::PointXYZI> main_plane_pca_for_3zone;
pcl::PointCloud<pcl::PointXYZI>::Ptr main_plane_pc_projected_(new pcl::PointCloud<pcl::PointXYZI>);
main_plane_pca_for_3zone.setInputCloud(main_plane_pc_projected);
main_plane_pca_for_3zone.project(*main_plane_pc_projected, *main_plane_pc_projected_);
compute3ZoneCovarianceMatrix(main_plane_pc_projected, main_plane_pca_for_3zone.getMean(), f.partial_covariance_2d); The cov-matrix from the upper zone from "main_plane_pc_projected" is defined as main_plane_prj_upper_cov_matrix. But i still do not understand why have you chose(0,0),(0,1),(1,1)elements of main_plane_upper_cov_matrix.How do you ensure that main_plane_upper_cov_matrix only have 3 unique elements. Finally, i wonder that why papers chose cov-matrix as the feature of points in 3 zone. I have known that cov-matrix is the key of PCA analysis and PCA analysis is the common way for data analysis. And as the original paper said that We focus on the points included in the main plane, to analyze the ### patterns that would correspond to the legs and trunk of a pedestrian, as shown in Fig 3, center top. These zones are the upper half, and the left and right lower halves. After separating the points into these zones, we calculate the covariance matrix (in 2D) over the transformed points laying inside each zone. This results in 9 additional features (3 unique values from each zone) How does cov-matrix represent the "patterns" of these zones. |
Hi, the implementation was followed the original paper Sec. 3.3.1 and the source code of |
Thank you for your replay. I have know that cov-matrix represent the correlation between variables which similar to variance。Thank you again. |
Thank you for your work in advance.
There are some questions about the feature 5 source code in the file of online_learning/object3d_detector/src/object3d_detector.cpp .
In the function Get3ZoneCovarianceMatrix, cov-matrix shape is 3x3, the elements used is (0,1) (0,2) and (1,2). Since cov-matrix is symmetric, there are nine unique elements, why did you only use (0,1) (0,2) and (1,2). For my sample, cov-matrix elements are all valid, none of them is near to zero.
In addtion, feature 5 is from the paper "Pedestrian Detection and Tracking Using Three-Dimensional LADAR Data". As description of the feature, the cov-matrix is 2D. Then i am confusion.
May i have you explain about the difference. Thank you
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