Object detection with false positive filtering

• US 8,761,446 B1
• Filed: 09/13/2012
• Issued: 06/24/2014
• Est. Priority Date: 03/10/2009
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Frome, Andrea, Cheung, German, Abdulkader, Ahmad, Zennaro, Marco, Wu, Bo, Neven, Hartmut, Vincent, Luc, Adam, Hartwig, Bissallo, Alessandro
• Primary Examiner(s): Lu, Tom Y
• Application Number:
  US13/614,822
• Time in Patent Office: 649 Days
• Field of Search: 382/103, 382/156, 382/173, 382/181
• US Class Current: 382/103
• CPC Class Codes: G06V 10/768 using context analysis, e.g. recognition aided by known co-occurring patterns
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/625 License plates
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation

Abstract

Embodiments of this invention relate to detecting and blurring images. In an embodiment, a system detects objects in a photographic image. The system includes an object detector module configured to detect regions of the photographic image that include objects of a particular type at least based on the content of the photographic image. The system further includes a false positive detector module configured to determine whether each region detected by the object detector module includes an object of the particular type at least based on information about the context in which the photographic image was taken.

Claims

1. A system for detecting objects in an image, comprising:

an object detector module configured to detect regions of the image based on at least the content of the image, wherein at least some of the regions include objects of a particular type; and

a false positive detector module configured to determine whether each region detected by the object detector module does not include an object of the particular type, based on at least a height of a camera with which the image was taken and an estimated average height of objects of the particular type,

wherein the object detector module and false positive detector module are implemented on at least one processor and memory.

2. The system of claim 1, wherein the object detector module is configured to detect regions that include faces.

3. The system of claim 1, wherein the object detector module is configured to detect regions that include license plates.

4. The system of claim 1, wherein the false positive detector module is configured to determine whether each region detected by the object detector module does not include an object of the particular type at least based on information describing the shape of the region.

5. The system of claim 1, wherein the false positive detector module is configured to determine whether each region detected by the object detector module does not include an object of the particular type at least based on information describing the appearance of the object in the region.

6. The system of claim 1, wherein the false positive detector module is configured to determine whether each region detected by the object detector module does not include an object of the particular type at least based on a degree to which the region overlaps with other regions in the image detected by the object detector module.

7. The system of claim 1, wherein the image is taken from a camera rosette that takes panoramic images, and

the information about the context in which the image was taken comprises information indicating which camera in the camera rosette took the image.

8. The system of claim 7, wherein the image is at least a portion of a street-level panoramic image.

9. The system of claim 1, wherein the false positive detector module comprises an artificial neural network.

10. The system of claim 9, wherein the artificial neural network is trained using regions detected by the object detector module in other images.

11. The system of claim 1, further comprising:

an image blurring module configured to blur the region of the image when the false positive detector module determines that the region includes an object of the particular type.

12. A method for detecting objects in an image, comprising:

(a) detecting, by one or more computing devices, regions of the image based on at least the content of the image, wherein at least some of the regions include objects of a particular type; and

(b) determining, by one or more computing devices, whether each region detected by the object detector module does not include an object of the particular type, based on at least a height of a camera with which the image was taken and an estimated average height of objects of the particular type.

13. The method of claim 12, wherein the detecting (a) comprises detecting regions that include faces.

14. The method of claim 12, wherein the detecting (a) comprises detecting regions that include license plates.

15. The method of claim 12, wherein the determining (b) comprises determining whether each region detected by the object detector module does not include an object of the particular type at least based on information describing the shape of the region.

16. The method of claim 12, wherein the determining (b) comprises determining whether each region detected by the object detector module does not include an object of the particular type at least based on information describing the appearance of the object in the region.

17. The method of claim 12, wherein the determining (b) comprises determining whether each region detected by the object detector module does not include an object of the particular type at least based on a degree to which the region overlaps with other regions in the image detected by the object detector module.

18. The method of claim 12, wherein the determining (b) comprises determining whether each region does not include an object using an artificial neural network, further comprising:

(c) training the artificial neural network using regions detected by an object detector module in other images.

19. The method of claim 12, further comprising:

(c) training an object detector module to detect the regions at an increased recall rate.