Tracking and surveillance in wide-area spatial environments using the abstract Hidden Markov Model

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex sp atial layout and the use of multiple sensors/cameras. To solve this problem , there is a need for representing the dynamic and noisy data in the tracki ng tasks, and dealing with them at different levels of detail. We employ th e Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidd en Markov Model (HMM) and a special type of Dynamic Probabilistic Network ( DPN), as our underlying representation framework. The AHMM allows us to exp licitly encode the hierarchy of connected spatial locations, making it scal able to the size of the environment being modeled. We describe an applicati on for tracking human movement in an office-like spatial layout where the A HMM is used to track and predict the evolution of object trajectories at di fferent levels of detail.