Object tracking system using statistical modeling and geometric relationship

Claim 3

The method of claim 1 wherein the step of generating the value set comprises generating at least a value indicating a correspondence between a portion of the first image and the statistical model, the characteristic, and the geometric relationship.

Claim 4

The method of claim 2 wherein the portion of the first image includes a pixel, and wherein the correspondence indicates a degree of matching between the pixel, the statistical model, and the characteristic.

Claim 5

The method of claim 1 wherein the statistical model includes a histogram of the first set of points.

Claim 6

The method of claim 4 wherein the characteristic includes a second histogram of the second set of points.

Claim 7

The method of claim 5 wherein the first set of points corresponds to a first region of the second image, the second set of points corresponds to a second region of the second image, and the geometric relationship corresponds to a predetermined distance between the first region and the second region.

Claim 8

The method of claim 6 wherein the second image comprises a plurality of pixels and wherein the predetermined distance corresponds to an offset in pixels between the first region and the second region.

Claim 9

The method of claim 5 wherein the histogram and the second histogram are color histograms.

Claim 10

The method of claim 5 further comprising the steps of: generating a third histogram of the first image; and prior to generating the value set, normalizing the histogram and the second histogram using the third histogram.

Claim 13

The method of claim 10 wherein the step of generating the value set comprises generating a value indicating a correlation between the first image and the first model, the second model, and the geometric relationship.

Claim 14

The method of claim 11 wherein generating the first model includes generating a first histogram of the first part, wherein generating the second model includes generating a second histogram of the second part, and the method further comprises: generating a third histogram of the first image; and prior to generating the value set, normalizing the first histogram and the second histogram using the third histogram.

Claim 15

The method of claim 12 wherein the normalizing the first histogram and the second histogram includes dividing the first histogram by the third histogram, and dividing the second histogram by the third histogram.

Claim 16

The method of claim 12 wherein the first image includes a plurality of color values, the first model includes a first histogram of the first part, the second model includes a second histogram of the second part, and generating the value indicating the correlation between the first part, the second part, the first model and the second model includes: accessing a first color value at a first location in the first image, the first color value having a first correspondence to the first histogram; accessing a second color value at a second location in the first image, the second color value having a second correspondence to the second histogram, the second location having a second geometric relationship with the first location, the second geometric relationship corresponding to the geometric relationship; determining a third correspondence between the first color value and the second color value using the first histogram and the second histogram; and determining the value from the first correspondence, the second correspondence, and the third correspondence.

Claim 17

The method of claim 10 wherein the object is a human, wherein the first part corresponds to the head of the human, and wherein the second part corresponds to the torso of the human.

Claim 18

The method of claim 10 further comprising the step of using a mouse pointing device to define the first part with a window.

Claim 19

The method of claim 10 wherein the accessing the second image includes receiving a the image from a video camera.

Claim 20

The method of claim 17 wherein the accessing the second image includes receiving the second image from a network.

Claim 21

The method of claim 17 wherein the accessing the second image includes extracting the second image from a digital movie.

Claim 24

The method of claim 20, wherein the step of generating the value set comprises generating at least one value indicating a first relationship between a first portion of the first image and the first color histogram, the one value also indicating a second relationship between a second portion of the first image and the second color histogram.

Claim 25

The method of claim 21 wherein the geometric relationship comprises an offset between the first region of the object and the second region of the object.

Claim 26

The method of claim 22, wherein the first portion of the first image has a first color, second portion of the first image has a second color and the step of generating as least one value further comprises: selecting a first population value from the first color histogram, the first population value corresponding to the first color; selecting a second population value from the second color histogram, the second population value corresponding to the second color; and determining a relationship between the first population value and the second population value; using the relationship to generate the one value.

Claim 27

The method of claim 21, wherein the first portion of the first image comprises a pixel.

Claim 28

The method of claim 21, wherein the first portion of the first image has a first color and the step of generating at least one value comprises: selecting a first population value from the first color histogram, the first population value corresponding to the first color; and using the first population value to generate the at least one value.

Claim 29

The method of claim 20, wherein the step of generating the first color histogram comprises the step of measuring hue and saturation values of the modeling image.

Claim 30

The method of claim 26, wherein the step of generating the first color histogram further comprises the step of measuring luminance values of the modeling image.

Claim 31

The method of claim 20, wherein the step of generating the first color histogram further comprises the step of measuring red, blue, and green values of the modeling image.

Claim 32

The method of claim 20, wherein the step of generating the first color histogram further comprises the step of determining a count of each occurrence of a color value of a set of colors.

Claim 33

An object tracking system for tracking an object in a tracking image, the object tracking system comprising: a camera; and a computer system coupled to the camera, the computer system having a memory, a processor, and an object identification procedure, the object identification procedure including, a modeling procedure that generates a first statistical model of a first region of the object and a second statistical model of a second region of the object based on a modeling image, the first region having a first geometric relationship with the second region, and a tracking procedure that locates the object at a location in the tracking image and generates a set of camera controls for the camera responsive to determining the location, the location having a first portion of the tracking image corresponding to the first statistical model and having a second portion of the tracking image corresponding to the second statistical model, wherein the first portion has a second geometric relationship with the second portion, the second geometric relationship corresponding to the first geometric relationship.

Claim 34

An object tracking system for tracking an object in a tracking image, the object tracking system comprising: a camera; and a computer system coupled to the camera, the computer system having a memory, a processor, and an object identification procedure, the object identification procedure including, a modeling procedure that generates a first statistical model of a first region of the object and a second statistical model of a second region of the object based on a modeling image, the first region having a first geometric relationship with the second region, and a tracking procedure that locates the object at a location in the tracking image and generates a set of camera controls for the camera responsive to determining the location, the location having a first portion of the tracking image corresponding to the first statistical model and having a second portion of the tracking image corresponding to the second statistical model, wherein the first portion has a second geometric relationship with the second portion, the second geometric relationship corresponding to the first geometric relationship.

Claim 35

The object tracking system of claim 30, further comprising a camera control procedure that uses the set of camera controls to control the camera in response to the object identification procedure.

Claim 36

The object tracking system of claim 30, wherein the first portion comprises a plurality of pixels.

Claim 37

The object tracking system of claim 30, wherein the first statistical model comprises a color histogram.

Claim 38

The object tracking system of claim 30, wherein the first statistical model comprises a texture histogram.

Claim 39

The object tracking system of claim 30, wherein the first statistical model comprises a intensity histogram.

Claim 40

The object tracking system of claim 30, wherein the modeling procedure generates second models of a second object and wherein the tracking procedure locates the second object at a second location in the tracking image using the second models.