Shadow detection in images
In an embodiment, a novel, scene adaptive, robust shadow detection method has been disclosed. An adaptive division image analysis compares one or more images containing objects and shadows to a reference image that is void of the objects and shadows. Ratios are calculated comparing the frame with shadows and/or objects to the reference frame. Shadows are identified by identifying pixels that fall within a certain range of these ratios. In another embodiment, projection histograms are used to identify the penumbra region of shadows in the image.
1. A process comprising:
using a processor for:
capturing a reference image;
capturing a current image;
calculating reference image to current image object plus shadow ratios (RTOPS);
calculating reference image to current image object ratios (RTO);
calculating reference image to current image shadow ratios (RTS);
calculating an average RTOPS ratio;
calculating an average RTO ratio;
calculating an average RTS ratio;
calculating a first range using said RTOPS, RTO, and RTS ratios;
identifying a pixel as being under a shadow by multiplying a lower limit of said first range by said average RTOPS ratio and multiplying an upper limit of said first range by said average RTOPS ratio, thereby giving a second range; and
determining whether said pixel falls within said second range.
2. The process of claim 1 , further comprising:
computing a column projection histogram comprising a count of the number of foreground pixels in each column;
computing a row projection histogram comprising a count of the number of foreground pixels in each row; and
identifying penumbra of a shadow by determining locations where said histogram counts are less than a threshold value.
3. The process of claim 1 , further comprising:
computing a column projection histogram comprising a count of the number of white pixels in each column;
computing a row projection histogram comprising a count of the number of white pixels in each row; and
identifying penumbra of a shadow by determining locations where said histogram counts are less than a threshold value.
4. The process of claim 1 , wherein said RTOPS ratios, said RTO ratios, and said RTS ratios are calculated by comparing the intensity of a pixel in said current image to the intensity of a corresponding pixel in said reference image.
5. The process of claim 1 , wherein said first range is calculated by comparing said average RTO ratio and said average RTS ratio to said average RTOPS ratio.
6. The process of claim 1 , wherein said reference image comprises a background image from background-foreground learning in a video motion detection algorithm.
7. The process of claim 1 , wherein said current image comprises:
an object;
a shadow associated with said object; and
a minimum bounded region comprising coordinates enclosing said object and said shadow.
8. A process comprising:
using a processor for:
capturing a reference image;
capturing a current image;
identifying a shadow in said current image using an adaptive image division analysis; and
identifying penumbra of said shadow using a projection histogram analysis;
wherein said adaptive image division analysis comprises:
calculating reference image to current image object plus shadow ratios (RTOPS);
calculating reference image to current image object ratios (RTO);
calculating reference image to current image shadow ratios (RTS);
calculating an average RTOPS ratio;
calculating an average RTO ratio;
calculating an average RTS ratio;
calculating a first range using said RTOPS, RTO, and RTS ratios;
identifying a pixel as being under a shadow by multiplying a lower limit of said first range by said average RTOPS ratio and multiplying an upper limit of said first range by said average RTOPS ratio, thereby giving a second range; and
determining whether said pixel falls within said second range.
9. The process of claim 8 , wherein said projection histogram analysis comprises:
computing a column projection histogram comprising a count of the number of foreground pixels in each column;
computing a row projection histogram comprising a count of the number of foreground pixels in each row; and
identifying penumbra of a shadow by determining locations where said histogram counts are less than a threshold value.
10. The process of claim 8 , further comprising:
computing a column projection histogram comprising a count of the number of white pixels in each column;
computing a row projection histogram comprising a count of the number of white pixels in each row; and
identifying penumbra of a shadow by determining locations where said histogram counts are less than a threshold value.
11. The process of claim 8 , wherein said RTOPS ratios, said RTO ratios, and said RTS ratios are calculated by comparing the intensity of a pixel in said current image to the intensity of a corresponding pixel in said reference image.
12. The process of claim 8 , wherein said first range is calculated by comparing said average RTO ratio and said average RTS ratio to said average RTOPS ratio.
13. The process of claim 8 , wherein said reference image comprises a background image from background-foreground learning in a video motion detection algorithm.
14. The process of claim 8 , wherein said current image comprises:
an object;
a shadow associated with said object; and
a minimum bounded region comprising coordinates enclosing said object and said shadow.
15. A computer readable memory device comprising instructions thereon for executing a process comprising:
capturing a reference image;
capturing a current image;
calculating reference image to current image object plus shadow ratios (RTOPS);
calculating reference image to current image object ratios (RTO);
calculating reference image to current image shadow ratios (RTS);
calculating an average RTOPS ratio;
calculating an average RTO ratio;
calculating an average RTS ratio;
calculating a first range using said RTOPS, RTO, and RTS ratios;
identifying a pixel as being under a shadow by multiplying a lower limit of said first range by said average RTOPS ratio and multiplying an upper limit of said first range by said average RTOPS ratio, thereby giving a second range; and
determining whether said pixel falls within said second range.
16. The computer readable memory device of claim 15 , further comprising instructions for:
computing a column projection histogram comprising a count of the number of foreground pixels in each column;
computing a row projection histogram comprising a count of the number of foreground pixels in each row; and
identifying penumbra of a shadow by determining locations where said histogram counts are less than a threshold value.
17. The computer readable memory device of claim 15 , further comprising instructions for:
computing a column projection histogram comprising a count of the number of white pixels in each column;
computing a row projection histogram comprising a count of the number of white pixels in each row; and
identifying penumbra of a shadow by determining locations where said histogram counts are less than a threshold value.
18. The computer readable memory device of claim 15 , wherein said RTOPS ratios, said RTO ratios, and said RTS ratios are calculated by comparing the intensity of a pixel in said current image to the intensity of a corresponding pixel in said reference image.
19. The computer readable memory device of claim 15 ,
wherein said first range is calculated by comparing said average RTO ratio and said average RTS ratio to said average RTOPS ratio; and further
wherein said reference image comprises a background image from background-foreground learning in a video motion detection algorithm.