Object classification in video data

• US 20070058836A1
• Filed: 09/15/2005
• Published: 03/15/2007
• Est. Priority Date: 09/15/2005
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Boregowda, Lokesh, Rajagopal, Anupama
• Application Number:
  US11/227,505
• Publication Number:
  US 20070058836A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69

Abstract

A method to classify objects labels objects as human, vehicle, multiple human, or other based on output from a motion detection algorithm. Features that are extracted from the blob, such as size, shape, and area, form a basis of the classification. The extracted features are subjected to various mathematical analyses that distinguish the classes that are available for labeling an object.

Claims

1. A method comprising:

receiving data regarding video motion detection and video motion tracking of an object;

identifying a blob in said data;

extracting fundamental features from said blob;

extracting miscellaneous features from said blob;

determining whether said blob meets a minimum object size;

applying a Fourier analysis to said blob, thereby producing a Fourier magnitude threshold;

providing one or more classifications for said blob;

computing a statistical weighted average for said one or more classifications based on said fundamental features and said miscellaneous features; and

computing a class confidence value for said one or more classifications.

2. The method of claim 1, further comprising:

rotating said blob; and

determining whether said Fourier magnitude threshold is exceeded.

3. The method of claim 1, wherein said blob does not satisfy said minimum object size, and further comprising labeling said blob as an other classification.

4. The method of claim 1, wherein said blob does not exceed said Fourier magnitude threshold, and further comprising labeling said blob as an other classification.

5. The method of claim 1, wherein said class confidence value exceeds a threshold for said one or more classifications, and further comprising assigning a label to said blob, thereby identifying said blob as a member of said one or more classifications.

6. The method of claim 1,

wherein said fundamental features include minimum bounding rectangle features (MBR) comprising an MBR length of said blob, an MBR width of said blob, an MBR area of said blob, and an MBR length to width ratio of said blob;

and

wherein said miscellaneous features include a projection histogram.

7. The method of claim 6,

wherein said fundamental features further comprise segment features and shape features, and further

wherein said segment features comprise an MBR segment perimeter of said blob, an MBR segment area of said blob, an MBR segment compactness of said blob, and an MBR fill ratio of said blob;

and

wherein said shape features comprise an MBR segment circularity of said blob, an MBR segment convexity of said blob, an MBR segment shape factor of said blob, an MBR segment elongation-indentation of said blob, and an MBR segment convex deviation of said blob.

8. The method of claim 6, further comprising:

calculating a normalized MBR length by dividing said MBR length by the number of pixel rows of said blob;

calculating a normalized MBR width by dividing said MBR width by the number of pixel columns of said blob;

calculating a normalized MBR area by multiplying said normalized MBR length by said normalized MBR width; and

calculating a normalized MBR length to width ratio by dividing said normalized MBR length by said normalized MBR width.

9. The method of claim 7, further comprising;

calculating a normalized MBR segment perimeter comprising

normalized MBR segment perimeter=(MBR segment perimeter)/(2*(blob pixel columns+blob pixel rows));

calculating a normalized MBR segment area comprising

normalized MBR segment area=(MBR segment area)/(blob pixel columns+blob pixel rows);

calculating a normalized MBR compactness comprising;

normalized MBR segment compactness=(MBR segment perimeter)²/MBR segment area; and

calculating an MBR fill ratio comprising

MBR fill ratio=MBR segment area/MBR area.

10. The method of claim 7, further comprising:

calculating an MBR segment circularity comprising

MBR segment circularity=(4*pi*MBR segment area)/ (MBR segment perimeter)²;

calculating an MBR segment convexity comprising

MBR segment convexity=MBR segment perimeter/(MBR segment area)^1/2;

calculating an MBR segment shape factor comprising

MBR segment shape factor=MBR segment area/(MBR segment perimeter)^0.589;

calculating an MBR elongation indent comprising

MBR elongation indent=[(MBR segment convexity)²+(MBR segment shape factor)²]^1/2; and

calculating an MBR segment shape factor convex deviation comprising

MBR segment shape factor convex deviation=arctangent (MBR segment shape factor/MBR segment convexity).

11. The method of claim 1, wherein said minimum object size (MOS) comprises:

MOS=(MBR length/[H²+V²]^1/2)/(2 tan<sup>−

1</sup>(d/2f));

wherein H is a horizontal distance from said object to an image capturing device;

wherein V is a vertical distance from said object to said image capturing device;

wherein d is the sensitivity area; and

wherein f is a focal length of said image capturing device.

12. The method of claim 1, further comprising calculating an axis of least second moment comprising:

$\mathrm{Tan}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(2<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\theta <br><br>}\right)=2<br><br>\frac{\mathrm{\Sigma <br><br>\Sigma <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{rcI}<br><br>\left(r,c\right)}{{\mathrm{\Sigma <br><br>\Sigma <br><br>r}}^2<br><br>I<br><br>\left(r,c\right)-{\mathrm{\Sigma <br><br>\Sigma <br><br>c}}^2<br><br>I<br><br>\left(r,c\right)}$

wherein r represents the number of pixel rows of said blob;

wherein c represents the number of pixel columns of said blob; and

wherein I(r,c) represents the center location of said blob.

13. The method of claim 1, further comprising:

providing a range of values for each of said classifications; and

associating said object with one of said classifications based on said range of values.

14. The method of claim 6, further comprising:

splitting said blob into four quadrants;

calculating a projection histogram representing said pixel rows;

calculating a projection histogram representing said pixel columns;

computing standard deviation values for said projection histograms;

weighting said projection histogram values; and

calculating values for said fundamental features, said segment features, and said shape features.

15. The method of claim 13, further comprising:

determining overlaps among said range of values;

calculating a total derived weight for said classifications based on a non-overlapping portion of said ranges and said overlapping portion of said ranges;

calculating a percentage derived weight based on said total derived weight and said range of values; and

classifying an object based on said percentage derived weight.

16. A method comprising:

receiving data regarding video motion detection and video motion tracking of an object;

identifying an orientation of said object;

aligning said object based on said orientation;

extracting shape features from said object;

providing limiting ranges for said shape features;

classifying said object based on said limiting ranges; and

labeling said object based on said classification.

17. The method of claim 16, further comprising:

deriving weights for said classification; and

calculating a confidence level for said classification, said confidence level based on said shape features from a plurality of images from said video motion detection data and said video motion tracking data.

18. A computer readable medium comprising instructions thereon for executing a method comprising:

receiving data regarding video motion detection and video motion tracking of an object;

computing a blob from said data;

rotating said blob;

extracting fundamental features from said blob;

extracting miscellaneous features from said blob;

determining whether said blob meets a minimum object size;

applying a Fourier analysis to said blob, thereby producing a Fourier magnitude threshold;

providing one or more classifications for said blob;

computing a statistical weighted average for said one or more classifications based on said fundamental features and said miscellaneous features; and

computing a class confidence value for said one or more classifications.

19. The computer readable medium of claim 18,

wherein said fundamental features include minimum bounding rectangle features (MBR) comprising an MBR length of said blob, an MBR width of said blob, an MBR area of said blob, and an MBR length to width ratio of said blob;

wherein said miscellaneous features include a projection histogram;

wherein said fundamental features further comprise segment features and shape features, and further

wherein said segment features comprise an MBR segment perimeter of said blob, an MBR segment area of said blob, an MBR segment compactness of said blob, and an MBR fill ratio of said blob; and

wherein said shape features comprise an MBR segment circularity of said blob, an MBR segment convexity of said blob, an MBR segment shape factor of said blob, an MBR segment elongation-indentation of said blob, and an MBR segment convex deviation of said blob.

20. The computer readable medium of claim 19, further comprising instructions for:

calculating a normalized MBR length by dividing said MBR length by the number of pixel rows of said blob;

calculating a normalized MBR width by dividing said MBR width by the number of pixel columns of said blob;

calculating a normalized MBR area by multiplying said normalized MBR length by said normalized MBR width;

calculating a normalized MBR length to width ratio by dividing said normalized MBR length by said normalized MBR width;

calculating a normalized MBR segment perimeter comprising

normalized MBR segment perimeter=(MBR segment perimeter)/(2*(blob pixel columns+blob pixel rows));

calculating a normalized MBR segment area comprising

normalized MBR segment area=(MBR segment area)/(blob pixel columns+blob pixel rows);

calculating a normalized MBR compactness comprising;

normalized MBR segment compactness=(MBR segment perimeter)²/MBR segment area;

calculating an MBR fill ratio comprising

MBR fill ratio=MBR segment area/MBR area;

calculating an MBR segment circularity comprising

MBR segment circularity=(4*pi*MBR segment area)/ (MBR segment perimeter)²;

calculating an MBR segment convexity comprising

MBR segment convexity=MBR segment perimeter/(MBR segment area)^1/2;

calculating an MBR segment shape factor comprising

MBR segment shape factor=MBR segment area/(MBR segment perimeter)^0.589;

calculating an MBR elongation indent comprising

MBR elongation indent=[(MBR segment convexity)²+(MBR segment shape factor)²]^1/2; and

calculating an MBR segment shape factor convex deviation comprising

MBR segment shape factor convex deviation=arctangent (MBR segment shape factor/MBR segment convexity).