2. An active learning system as set forth in claim 1 , further comprising: a flow-based object segmentation module for segmenting a potential object candidate from a sequence of video frames taken from a scene, and communicating the segmented potential object candidate to the oriented localized filter module for iconic feature extraction; and an incremental learning module for predicting a general class of the potential object candidate.
2. An active learning system as set forth in claim 1 , further comprising: a flow-based object segmentation module for segmenting a potential object candidate from a sequence of video frames taken from a scene, and communicating the segmented potential object candidate to the oriented localized filter module for iconic feature extraction; and an incremental learning module for predicting a general class of the potential object candidate. 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.