Fusion-based object-recognition

• US 9,721,162 B2
• Filed: 06/16/2015
• Issued: 08/01/2017
• Est. Priority Date: 06/16/2014
• Status: Active Grant

• Current Assignee: AGT International GMBH
• Original Assignee: AGT International GMBH
• Inventors: Huber, Marco, Merentitis, Andreas, Debes, Christian, Heremans, Roel
• Primary Examiner(s): Johns, Andrew W
• Application Number:
  US14/740,300
• Publication Number:
  US 20150363643A1
• Time in Patent Office: 777 Days
• CPC Class Codes: G06F 18/25 Fusion techniques
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging

Abstract

An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Claims

1. A fusion-based method of object recognition comprising:

capturing a first object image with a first image capture detector;

generating a first score indicative of a first probability that the first object image corresponds to a data base image; and

using a processor to apply a Bayesian fusion algorithm to revise the first probability based on a second score of a second object image indicative of a second probability that the second object image corresponds to the data base image, captured by a second image capture detector, other than the first image capture detector,

wherein said using a processor to apply the Bayesian fusion algorithm further includes applying a transition model, configured to calculate a transition probability of an object moving from the first image capture detector to the second image capture detector in time t, wherein said time t is the time difference between a capture time of the first object image and a capture time of the second object image to revise the first probability.

2. The fusion-based method of claim 1, wherein the processor is further configured to use credibility ratings associated with the first object image and the second object image to revise the first probability.

3. The fusion-based method of claim 1, wherein generating a first or second score indicative of a first or second probability that the first or second object image corresponds to a data base image of a person i is calculated with i=0, . . . , n, is:

4. A fusion-based, object-recognition system comprising:

a plurality of image capture detectors, each of the image capture detectors configured to generate a first score indicative of a first probability of correspondence between a first object image it captured and an image in a data base;

a processor configured to apply a Bayesian fusion algorithm to revise the first probability based on a second score of a second object image indicative of a second probability that the second object image corresponds to the image in the data base, captured by a second image capture detector, wherein the processor is further configured to apply a transition model configured to calculate a transition probability of an object moving from the first image capture detector to the second image capture detector in time t, wherein said time t is the time difference between a capture time of the first object image and a capture time of the second object image to revise the first probability.

5. The fusion-based object-recognition system of claim 4, wherein the processor is further configured to use credibility ratings associated with the first object image and the second object image to revise the probability.