1. A computer-implemented method for analyzing a sequence of video frames depicting a scene captured by a video camera, the method comprising: receiving one or more data streams generated from the sequence of video frames, wherein a first one of the data streams provides a stream of context events generated by a computer vision engine, and wherein each context event provides kinematic data related to a foreground object observed by the computer vision engine in the sequence of video frames; parsing the data streams to identify data inputs matching an input layer of one of a plurality of adaptive resonance theory (ART) networks, wherein each ART network is configured to generate clusters from the data inputs matching the input layer of a respective ART network, and wherein each cluster provides a statistical distribution of a characteristic of the scene derived from the data streams that has been observed to occur at a location in the scene corresponding to a location of the cluster; passing the data inputs to the ART network with the matching input layer; updating the generated clusters in the ART network with the matching input layer; and evaluating the clusters of the ART network with the matching input layer to determine whether the data inputs passed to the ART network are indicative of an occurrence of a statistically relevant event, relative to the clusters in the ART network with the matching input layer; and upon determining that a first one of the clusters in a first one of the plurality of ART networks has not been updated for a specified period of time, removing the first cluster from the first ART network.