Estimating rainfall precipitation amounts by applying computer vision in cameras

Patent metadata

Field	Value
Publication number	US-2016148382-A1
Application number	US-201414551238-A
Country	US
Kind code	A1
Filing date	Nov 24, 2014
Priority date	Nov 24, 2014
Publication date	May 26, 2016
Grant date	—

Abstract

A method and system are provided. The method includes storing a set of references images without rain and spanning a plurality of different light conditions. The method further includes capturing, using a camera, an image of a scene with rain. The method also includes selecting a reference image from the set of reference images based on the light condition of the captured image. The method additionally includes performing an arithmetic subtraction image processing operation between the captured image and the reference image to generate a subtraction image. The method further includes estimating an amount of rain in the subtraction image based on previously calibrated values.

First claim

1 - 17 . (canceled)

18 . A computer program product for estimating rainfall precipitation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
storing a set of references images without rain and spanning a plurality of different light conditions; capturing, using a camera, an image of a scene with rain; selecting a reference image from the set of reference images based on the light condition of the captured image; performing an arithmetic subtraction image processing operation between the captured image and the reference image to generate a subtraction image; and estimating an amount of rain in the subtraction image based on previously calibrated values.

19 . A system, comprising:
a memory for storing a set of references images without rain and spanning a plurality of different light conditions; a camera for capturing an image of a scene with rain; a processor for selecting a reference image from the set of reference images based on the light condition of the captured image, performing an arithmetic subtraction image processing operation between the captured image and the reference image to generate a subtraction image, and estimating an amount of rain in the subtraction image based on previously calibrated values.

20 . The system of claim 19 , wherein the processor performs image border detection processing on the subtraction image to detect the rain and generates a binary mask responsive to a result of the subtraction image processing operation, the binary mask having a first pixel value indicative of an absence of rain and having a second pixel value indicative of a presence of rain, and wherein the processor performs a direct count of pixels having rain by counting a total number of occurrences of the second pixel value, and generates an estimate of the amount of rain responsive to the direct count of pixels.

21 . The computer program product of claim 18 , wherein the set of reference images is captured at least one day prior to capturing the image of the scene with rain.

22 . The computer program product of claim 18 , further comprising obtaining the set of reference images from a remote source with respect to the camera.

23 . The computer program product of claim 18 , wherein the reference image is selected from the set of reference images based on the light condition of the captured image using a time of day as a selection basis.

24 . The computer program product of claim 18 , wherein the reference image is selected from the set of reference images based on the light condition of the captured image using a day of the year as a selection basis.

25 . The computer program product of claim 18 , wherein the arithmetic subtraction image processing operation comprises performing:
C ( x,y )− T ( x,y )= O ( x,y ),
where C denotes the captured image, T denotes the reference image, O denotes the subtraction image without a background and with at least one moving object, x denotes a first spatial orientation, and y denotes a second spatial orientation orthogonal with respect to the first spatial orientation.

26 . The computer program product of claim 18 , wherein said step of estimating the amount of rain comprises setting a predetermined granularity and precision for at least one rain estimate.

27 . The system of claim 19 , wherein the set of reference images is captured at least one day prior to capturing the image of the scene with rain.

28 . The system of claim 19 , wherein the processor obtains the set of reference images from a remote source with respect to the camera.

29 . The system of claim 19 , wherein the reference image is selected from the set of reference images based on the light condition of the captured image using a time of day as a selection basis.

30 . The system of claim 19 , wherein the reference image is selected from the set of reference images based on the light condition of the captured image using a day of the year as a selection basis.

31 . The system of claim 19 , wherein the arithmetic subtraction image processing operation comprises performing:
C ( x,y )− T ( x,y )= O ( x,y ),
where C denotes the captured image, T denotes the reference image, 0 denotes the subtraction image without a background and with at least one moving object, x denotes a first spatial orientation, and y denotes a second spatial orientation orthogonal with respect to the first spatial orientation.

32 . The system of claim 19 , wherein a predetermined granularity and precision are set for at least one rain estimate.

Assignees

• IBM

Inventors

• Cerqueira Renato F
• Mantripragada Kiran

Classifications

• G01W1/14 Primary

  Rainfall or precipitation gauges (measuring volume in general G01F) · CPC title

• H04N23/80

  Camera processing pipelines; Components thereof · CPC title

• G06V10/42

  Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation · CPC title

• G06V10/48

  by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation · CPC title

• H04N23/951

  by using two or more images to influence resolution, frame rate or aspect ratio · CPC title