Statistical modeling and performance characterization of a real-time dual camera surveillance system

1. A method for visually locating and tracking an object through a space, comprising the steps of:

restricting an area of the space to be searched to a plurality of regions with a high probability of significant change, the space being defined in images supplied by a camera;

deriving statistical models for errors, including quantifying an indexing step performed by an indexing module, and tuning system parameters, wherein each statistical model is a candidate hypothesis for object location;

applying a likelihood model for candidate hypothesis evaluation; and

locating the object according to a candidate hypothesis evaluated to satisfy the likelihood model.

2. The method of claim 1 , wherein restricting the area of the space to be searched further comprises the steps of:

applying a calibration module for determining a static scene;

applying an illumination-invariant module for tracking image transformation; and

applying the indexing module for selecting regions of interest for hypothesis generation.

3. The method of claim 2 , further comprising the steps of:

applying a statistical estimation module for estimating a number of objects and their positions; and

applying a foveal camera control module for estimating a plurality of control parameters of a foveal camera based on location estimates and uncertainties.

4. The method of claim 2 , further comprising the step of applying a background adaptation module for detecting and tracking the object in dynamically varying illumination situations.

5. The method of claim 1 , wherein an application specific module is provided based on a plurality of prior distributions for imposing restrictions on the candidate hypotheses.

6. The method of claim 5 , wherein the plurality of prior distributions comprise:

an object geometry model;

a camera geometry model;

a camera error model; and

an illumination model.

7. The method of claim 1 , wherein the camera is an omnicamera.

8. The method of claim 1 , wherein the object is tracked using a foveal camera.

9. The method of claim 1 , wherein the step of deriving statistical models is applied a plurality of times to achieve a given probability of misdetection and false alarm rate.

10. The method claim 9 , further comprising the step of validating a theoretical model for the space monitored for determining correctness and closeness to reality.

11. The method of claim 1 , wherein the indexing module selects a plurality of regions with a high probability of significant change, motivated by a plurality of two dimensional image priors induced by a plurality of prior distributions in the space, wherein the space is three dimensional.

12. The method of claim 1 , further comprising estimating an uncertainty of the object's parameters for predicting a system's performance and for automating control of the system.

13. The method of claim 1 , employed in an automobile wherein the space monitored comprises one of an interior compartment of the automobile and an exterior of the automobile.

14. The method of claim 1 , wherein the candidate hypothesis evaluated that satisfies the likelihood model minimizes a Bayes error.

15. A computer program product comprising computer program code stored on a computer readable storage medium for, for locating and tracking objects through a space, the computer program product comprising:

computer readable program code for causing a computer to restrict an area of the space to be search to a plurality of regions with a high probability of significant change within the space;

computer readable program code for causing a computer to derive statistical models for errors, including quantifying an indexing step, and tuning system parameters, wherein each statistical model is a candidate hypothesis for object location;

computer readable program code for causing a computer to apply a likelihood model for candidate hypothesis evaluation; and

computer readable program code for causing a computer to locate the object according to a candidate hypothesis evaluated to satisfy the likelihood model.