Method and apparatus for differentiating pedestrians, vehicles, and other objects

• US 20050232491A1
• Filed: 03/02/2005
• Published: 10/20/2005
• Est. Priority Date: 03/02/2004
• Status: Active Grant

• Current Assignee: ZAMA Innovations LLC
• Original Assignee: Sarnoff Corporation (SRI International, Inc.)
• Inventors: Chang, Peng, Camus, Theodore Armand
• Granted Patent: US 7,672,514 B2
• Time in Patent Office: Days
• US Class Current: 382/199
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30261 Obstacle
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Abstract

A method and apparatus for classifying an object in an image is disclosed. Edges of an object are detected within a region of interest. Edge analysis is performed on a plurality of sub-regions within the region of interest to generate an edge score. The object is classified based on the edge score.

Claims

1. A method of classifying an object in an image, comprising:

detecting edges of said object within a region of interest;

performing edge analysis on a plurality of sub-regions within said region of interest to generate an edge score; and

classifying said object based on said edge score.

2. The method of claim 1, wherein detecting said edges comprises performing Canny edge detection.

3. The method of claim 1, wherein said plurality of sub-regions comprise a top region, a bottom region, a left region, and a right region.

4. The method of claim 1, wherein said plurality of sub-regions comprise a head region, a left upper body region, a right upper body region, a left lower body region, and a right lower body region.

5. The method of claim 1, wherein said edge analysis determines an edge energy for each subregion.

6. The method of claim 5, wherein said edge score comprises a sum of the edge energy for each sub-region.

7. The method of claim 1, wherein said object is classified in accordance with a threshold for said edge score.

8. An apparatus for classifying an object in an image, comprising:

means for detecting edges of said object within a region of interest;

means for performing edge analysis on a plurality of sub-regions within said region of interest to generate an edge score; and

means for classifying said object based on said edge score.

9. The apparatus of claim 8, wherein detecting said edges comprises performing Canny edge detection.

10. The apparatus of claim 8, wherein said plurality of sub-regions comprise a top region, a bottom region, a left region, and a right region.

11. The apparatus of claim 8, wherein said plurality of sub-regions comprise a head region, a left upper body region, a right upper body region, a left lower body region, and a right lower body region.

12. The apparatus of claim 8, wherein said edge analysis determines an edge energy for each subregion.

13. The apparatus of claim 12, wherein said edge score comprises a sum of the edge energy for each sub-region.

14. The apparatus of claim 8, wherein said object is classified in accordance with a threshold for said edge score.

15. A computer-readable medium having stored thereon a plurality of instructions, the plurality of instructions including instructions which, when executed by a processor, cause the processor to perform the steps of a method of classifying an object in an image, comprising:

detecting edges of said object within a region of interest;

performing edge analysis on a plurality of sub-regions within said region of interest to generate an edge score; and

classifying said object based on said edge score.

16. The computer-readable medium of claim 15, wherein detecting said edges comprises performing Canny edge detection.

17. The computer-readable medium of claim 15, wherein said plurality of sub-regions comprise a top region, a bottom region, a left region, and a right region.

18. The computer-readable medium of claim 15, wherein said plurality of sub-regions comprise a head region, a left upper body region, a right upper body region, a left lower body region, and a right lower body region.

19. The computer-readable medium of claim 15, wherein said edge analysis determines an edge energy for each subregion.

20. The computer-readable medium of claim 15, wherein said object is classified in accordance with a threshold for said edge score.