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

Abstract

The present invention relates to a method for visually detecting and tracking an object through a space. The method chooses modules for a restricting a search function within the space to regions with a high probability of significant change, the search function operating on images supplied by a camera. The method also derives statistical models for errors, including quantifying an indexing step performed by an indexing module, and tuning system parameters. Further the method applies a likelihood model for candidate hypothesis evaluation and object parameters estimation for locating the object.

1 . A method for visually locating and tracking an object through a space, comprising the steps of: 
 choosing a plurality of modules for restricting a search function within the space to a plurality of regions with a high probability of significant change, the search function operating on images supplied by a camera;    deriving statistical models for errors, including quantifying an indexing step performed by an indexing module, and tuning system parameters; and    applying a likelihood model for candidate hypothesis evaluation and object parameters estimation for locating the object.    
   
   
       2 . The method of  claim 1 , wherein the step of choosing the plurality of modules 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 each module is application specific based on a plurality of prior distributions for imposing restrictions on a search function.  
   
   
       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 , wherein the step of applying a likelihood model further comprises the step of 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 . 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 choose a plurality of modules for a restricting search functions within a context 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; and    computer readable program code for causing a computer to apply a likelihood model for candidate hypothesis evaluation and object parameters estimation for locating the object.