Method and apparatus for unsupervised learning of discriminative edge measures for vehicle matching between non-overlapping cameras
First Claim
Patent Images
1. A computer implemented method for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras, comprising:
- using a processor to perform the steps of;
collecting at least two pairs of feature maps, wherein the at least two pairs of feature maps are derived from features of objects captured in the images from the at least two non-overlapping cameras;
computing, as a function of the at least two pairs of feature maps, at least first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class, and wherein objects in the same class are deemed to match; and
learning from the at least two pairs of feature maps outlier and inlier distributions, wherein determining the first and second match measures, comprises;
computing, as a function of the outlier and inlier distributions, a weight for each of the first and second match measures, and probabilities for the first and second match measures being of the same and different classes.
2 Assignments
0 Petitions
Accused Products
Abstract
A method and apparatus for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras is disclosed The method includes collecting at least two pairs of feature maps, where the at least two pairs of feature maps are derived from features of objects captured in the images. The method further includes computing, as a function of at least two pairs of feature maps, at least one first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class.
-
Citations
18 Claims
-
1. A computer implemented method for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras, comprising:
using a processor to perform the steps of; collecting at least two pairs of feature maps, wherein the at least two pairs of feature maps are derived from features of objects captured in the images from the at least two non-overlapping cameras; computing, as a function of the at least two pairs of feature maps, at least first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class, and wherein objects in the same class are deemed to match; and learning from the at least two pairs of feature maps outlier and inlier distributions, wherein determining the first and second match measures, comprises;
computing, as a function of the outlier and inlier distributions, a weight for each of the first and second match measures, and probabilities for the first and second match measures being of the same and different classes.- View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
9. An apparatus for matching objects between images from at least two non-overlapping cameras, comprising:
-
means for collecting at least two pairs of feature maps, wherein the at least two pairs of feature maps are derived from features of objects captured in the images; and means for computing, as a function of the at least two pairs of feature maps, at least first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class, and wherein objects in the same class are deemed to match; and means for learning from the at least one two pairs of feature maps outlier and inlier distributions, wherein the means for computing the first and second match measures comprises;
means for computing, as a function of the outlier and inlier distributions, a weight for each of the first and second match measures, and probabilities for the first and second match measures being of the same and different classes. - View Dependent Claims (10, 11, 12, 13, 14)
-
-
15. A computer implemented method for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras, comprising:
using a processor to perform the steps of; collecting at least two pairs of feature maps, wherein the at least two pairs of feature maps are derived from features of objects captured in the images from the at least two non-overlapping cameras; learning from the at least two pairs of feature maps respective outlier and inlier distributions; and computing, as a function of the at least two pairs of feature maps and the respective outlier and inlier distributions, at least first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class, and wherein objects in the same class are deemed to match. - View Dependent Claims (16, 17, 18)
Specification