Statistical modeling and performance characterization of a real-time dual camera surveillance system
AssigneeSiemens Corporate Research, Inc.
InventorsMichael Greiffenhagen, Visvanathan Ramesh, Dorin Comaniciu
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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.
Claims15
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.
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.
Dependencies are inferred from the claim text.