8. An active learning system as set forth in claim 3 , 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.
3. An active learning system as set forth in claim 2 , further comprising: a fixed-basis function decomposition module using Haar wavelets to extract a relevant feature set from the potential object candidate; a static classifier for initial or temporary classification of the potential object candidate, 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.