LOW- AND HIGH-FIDELITY CLASSIFIERS APPLIED TO ROAD-SCENE IMAGES
First Claim
1. A system, comprising:
- a low-fidelity classifier, on a processor set, operable to select a candidate region, from a region set spanning a down-sampled version of an image from an automobile-affixed camera capturing road-scenes, upon determining the candidate depicts a classified object;
a high-fidelity classifier, on the processor set, operable to verify classified-object depiction in a patch, mapped from the candidate, of a high-fidelity version of the image, where the high-fidelity classifier indicates the depiction.
1 Assignment
0 Petitions
Accused Products
Abstract
Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.
60 Citations
20 Claims
-
1. A system, comprising:
-
a low-fidelity classifier, on a processor set, operable to select a candidate region, from a region set spanning a down-sampled version of an image from an automobile-affixed camera capturing road-scenes, upon determining the candidate depicts a classified object; a high-fidelity classifier, on the processor set, operable to verify classified-object depiction in a patch, mapped from the candidate, of a high-fidelity version of the image, where the high-fidelity classifier indicates the depiction. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A method for object classification and location, comprising:
-
down-sampling an image to a down-sampled version of the image; extracting a set of overlapping zones covering the down-sampled version, as definable by a sliding window with dimensions equal to dimensions of the zones; selecting a probable zone from the set of overlapping zones for which a low-fidelity classifier, comprising a first Convolution Neural Network (CNN), indicates a probability of a presence of an object pertaining to a class of objects classifiable by the low-fidelity classifier; mapping the probable zone selected from the down-sampled version to a sector of a higher-resolution version of the image; and confirming the presence of the object by applying the sector to a high-fidelity classifier, comprising a second CNN, where applying the sector indicates the presence. - View Dependent Claims (11, 12, 13, 14, 15, 16)
-
-
17. An image-analysis system, comprising:
-
at least one database, on at least one storage medium, comprising; a first dataset comprising cropped, down-sampled images with labels of a label set; a second dataset comprising cropped, higher-resolution images with the labels from the label set; and a processor set implementing; a first Convolution Neural Network (CNN) operable to be trained on the first dataset to classify, relative to the label set, a section from a set of overlapping sections spanning a down-sampled version of a road-scene image, section dimensions being commensurate to dimensions of the down-sampled images; and a second CNN, operable to be trained on the second dataset to re-classify, relative to the label set, an area of the road-scene image, at high fidelity, covering the section. - View Dependent Claims (18, 19, 20)
-
Specification