Automatic hierarchical classification using time-based co-occurrences

Abstract

While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by using accumulated joint cooccurrences of the representations within the sequence to create a hierarchical binary-tree classifier of the representations. This classifier is useful to classify sequences as well as individual instances. We illustrate the use of this method on two separate representations the tracked object's position, movement, and size; and the tracked object's binary motion silhouettes.