15. A computer program product as set forth in claim 14 , further comprising computer-readable instruction means encoded on a computer-readable medium for causing a computer to: extract a relevant feature set from the potential object candidate using a fixed-basis function decomposition module using Haar wavelets; classify the potential object candidate using a static classifier, thereby generating a classification category selected from a group consisting of a positive identification, a negative identification, a false positive identification, and a false negative identification, and where upon classification, the static classifier communicates the classification to the incremental learning module.
15. A computer program product as set forth in claim 14 , further comprising computer-readable instruction means encoded on a computer-readable medium for causing a computer to: extract a relevant feature set from the potential object candidate using a fixed-basis function decomposition module using Haar wavelets; classify the potential object candidate using a static classifier, thereby generating a classification category selected from a group consisting of a positive identification, a negative identification, a false positive identification, and a false negative identification, and where upon classification, the static classifier communicates the classification to the incremental learning module. 20. A computer program product as set forth in claim 15 , wherein the flow-based object segmentation module is configured to: receive an input video sequence consisting of a sequence of frames; compute points to track from the video sequence using a Harris corner detector; compute an optical flow from the points by utilizing a Lucas Kanade approach in which a pyramid of the input sequence of frames is created to compute a two-dimensional (2D) optical flow field consisting of rows or columns at different levels using a fixed size neighborhood, where flow computed at coarser levels is then propagated to finer resolution levels; generate a 2D flow histogram image by converting each row or column of the 2D optical flow field into a one-dimensional (1D) flow histogram and stacking the flow histograms row-wise or column-wise; post-process the flow histogram image by utilizing a modified median filter on the flow histogram image to eliminate isolated non-zero bins; detect background flow from the flow histogram using a Hough transform; estimate object dimensions from the flow histogram image using the Hough transform; update the object dimension estimates by creating an average object dimension estimate as new object dimension estimates become available; and segment the object from the scene using the object dimension estimates, the segmented object being the object candidate in the form of an object model.