METHOD AND SYSTEM FOR DETECTING AND TRACKING HANDS IN AN IMAGE

• US 20110299774A1
• Filed: 04/22/2008
• Published: 12/08/2011
• Est. Priority Date: 04/22/2008
• Status: Abandoned Application

• Current Assignee: Corey Mason Manders, Farzam Farbiz, Jyn Herng Bryan Chong, Ka Yin Christina Tang
• Original Assignee: Corey Mason Manders, Farzam Farbiz, Jyn Herng Bryan Chong, Ka Yin Christina Tang
• Inventors: Manders, Corey Mason, Farbiz, Farzam, Chong, Jyn Herng Bryan, Tang, Ka Yin Christina
• Application Number:
  US12/988,936
• Publication Number:
  US 20110299774A1
• Time in Patent Office: Days
• US Class Current: 382/168
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06V 40/107 Static hand or arm

Abstract

A method and system for detecting and tracking hands in an image. The method for detecting and tracking hands in an image comprises the steps of calculating a first probability map comprising of probabilities that respective pixels in the image corresponds to skin based on colour information associated with the respective pixels; calculating a second probability map comprising of probabilities that the respective pixels in the image corresponds to a part of a hand based on depth information associated with the respective pixels; calculating a joint probability map by combining the first probability map and the second probability map; and detecting and tracking hands in the image using an algorithm with a weight output as a detection threshold applied on the joint probability map.

Claims

1. A method for detecting and tracking hands in an image, the method comprising the steps of:

calculating a first probability map comprising of probabilities that respective pixels in the image corresponds to skin based on colour information associated with the respective pixels;

calculating a second probability map comprising of probabilities that the respective pixels in the image corresponds to a part of a hand based on depth information associated with the respective pixels;

calculating a joint probability map by combining the first probability map and the second probability map; and

detecting and tracking hands in the image using an algorithm with a weight output as a detection threshold applied on the joint probability map.

2. The method as claimed in claim 1, wherein the step of calculating a first probability map comprising of probabilities that respective pixels in the image correspond to skin based on colour information associated with the respective pixels further comprises the steps of:

detecting a face in the image;

calculating hue and saturation values of the respective pixels in the image;

quantizing the hue and saturation values calculated;

constructing a histogram by using the quantized hue and saturation values of the respective pixels in a subset of pixels from a part of the detected face;

transforming the histogram into a probability distribution via normalization; and

back projecting the probability distribution onto the image in the hue/saturation space to obtain the first probability map.

3. The method as claimed in claim 2, wherein the step of calculating hue and saturation components of the respective pixels in the image further comprises the step of applying the inverse of a range compression function to the respective pixels in the image.

4. The method as claimed in claim 2, further comprising the step of building a mask for the detected face prior to using the subset of pixels from a part of the detected face to construct the histogram, wherein the mask removes pixels not corresponding to skin from the subset of pixels.

5. The method as claimed in claim 2, further comprising the step of adding the constructed histogram to a set of previously constructed histograms to form an accumulated histogram prior to transforming the histogram into a probability distribution via normalization.

6. The method as claimed in claim 1, further comprising the steps of:

defining the horizontal aspect of a ROI of a right hand to be the right side of the image starting slightly from the right of the face to the right edge of the image;

defining the horizontal aspect of a ROI of a left hand to be the left side of the image starting slightly from the left of the face to the left edge of the image; and

defining the vertical aspect of a ROI of both hands to be from just above a head containing the face to the bottom of the image; and

the back projecting of the probability distribution onto the image in the hue/saturation space to obtain the first probability map is performed onto candidate regions of the image corresponding to the ROI.

7. The method as claimed in claim 1, wherein if a face is not detected, the method further comprises the steps of:

checking if the hands are detected in a previous frame;

checking if a ROI of the hands is close to a ROI of the face in the previous frame;

defining a ROI of the hands in a current frame based on the ROI of the hands in the previous frame if the hands are detected in the previous frame and if the ROI of the hands are close to the ROI of the face in the previous frame; and

the back projecting of the probability distribution onto the image in the hue/saturation space to obtain the first probability map is performed onto candidate regions of the image corresponding to the ROI of the hands in the current frame.

8. The method as claimed in claim 1, wherein the step of calculating a second probability map comprising of probabilities that the respective pixels in the image corresponds to a part of a hand based on depth information associated with the respective pixels further comprises the steps of:

calculating a first distance, d_face, between a face and a camera;

calculating a second distance, d_min, wherein the second distance is the minimum distance an object can be from the camera;

calculating a third distance, D, between the respective pixels in the image and the camera;

calculating a probability of zero if D is greater than d_face, a probability of one if the D is less than d_min and a probability of (d_face−

D)/(d_face−

d_min) otherwise for the respective pixels in the image;

normalizing the calculated probability by multiplying said calculated probability by (2/(d_face+d_min) for the respective pixels in the image;

calculating pixel disparity values resulting from a plurality of cameras having differing spatial locations; and

converting the normalized probability into a probability that the respective pixels in the image corresponds to a part of a hand using the pixel disparity values to form the second probability map.

9. The method as claimed in claim 1, wherein the step of calculating a joint probability map by combining the first probability map and the second probability map further comprises the step of multiplying the first probability map and the second probability map by using Hadamard product.

10. The method as claimed in claim 1, the method further comprising the step of applying a mask over the joint probability map prior to detecting hands in the image, wherein the mask is centered on a last known hand position.

11. The method as claimed in claim 1, wherein the step of detecting and tracking hands in the image using the algorithm with a weight output as a detection threshold applied on the joint probability map further comprises the steps of:

calculating a central point of a rectangle around each of a probability mass along with the angle of each of a probability mass in the joint probability map in this frame; and

calculating a position of each of the hands in the X, Y and Z axes as well as the angle of each hand using the calculated central point and calculated angle in this frame, and the calculated central point in the previous frame.

12. The method as claimed in claim 1, the method further comprising the step of calculating the direction and velocity of motion of the detected hands using the positions of previously detected hands and the positions of following detected hands.

13. A system for detecting and tracking hands in an image, the system comprising:

a first probability map calculating unit for calculating a first probability map comprising of probabilities that respective pixels in the image corresponds to skin based on colour information associated with the respective pixels;

a second probability map calculating unit for calculating a second probability map comprising of probabilities that the respective pixels in the image corresponds to a part of a hand based on depth information associated with the respective pixels;

a third probability map calculating unit for calculating a joint probability map by combining the first probability map and the second probability map; and

a detecting unit for detecting and tracking hands in the image using an algorithm with a weight output as a detection threshold applied on the joint probability map.

14. The system as claimed in claim 13, the system further comprising an expander for applying the inverse of a range compression function to the respective pixels in the image.

15. A non-transitory data storage medium having stored thereon computer code means for instructing a computer system to execute a method for detecting hands in an image, the method comprising the steps of:

calculating a first probability map comprising of probabilities that respective pixels in the image corresponds to skin based on colour information associated with the respective pixels;

calculating a second probability map comprising of probabilities that the respective pixels in the image corresponds to a part of a hand based on depth information associated with the respective pixels;

calculating a joint probability map by combining the first probability map and the second probability map; and

detecting and tracking hands in the image using an algorithm with a weight output used as a detection threshold applied on the joint probability map.