18. A system, comprising: a video input source configured to provide image data; a processor; and a memory containing a program, which, when executed on the processor is configured to perform an operation for extracting pixel-level micro-features from the image data captured by the video input source, the operation comprising: receiving the image data; identifying a set of pixels in the image data associated with a foreground patch that depicts a foreground object; evaluating appearance values of the pixels included in the set of pixels to compute a plurality of micro-feature values representing the foreground object, each based on at least one pixel-level characteristic of the foreground patch, wherein the micro-feature values are computed independent of training data that defines a plurality of object types; generating a micro-feature vector that includes the micro-feature value; classifying the foreground object as depicting an object type as based on the micro-feature vector, wherein the object type is determined by mapping the micro-feature vector to a cluster in a self-organizing map (SOM) adaptive resonance theory (ART) network generated from a plurality of micro-feature vectors; and updating one or more cluster properties associated with the cluster based on the plurality of micro-feature values in the generated micro-feature vector.
23. The system of claim 18 , wherein one of the computed micro-feature values is a verticality value and the pixel-level characteristic is instantaneous per-pixel gradients of the foreground patch.