There are many useful observable characteristics of the state of a tracked object. These characteristics could include normalized size, normalized speed, normalized direction, object color, position, and object shape among other characteristics. Although these characteristics are by no means completely independent of each other, it is desirable to determine a separate, compact description of each of each of these aspects. Using this compact factored description, different aspects of individual sequences can be estimated and described without overwhelming computational or storage costs. In this work, we describe Factored Latent Analysis (FLA) and its application to deriving factored models for segmenting sequences in each of K separate characteristics. This method exploits temporally local statistics within each of the latent aspects and their inter-dependencies to derive a model that allows segmentation of each of the observed characteristics. This method is data driven and unsupervised. Activity classification results for multiple challenging environments are shown.
['Chris Stauffer']
Learning a Factorized Segmental Representation of Far-Field Tracking Data