Inferring Complex Agent Motions from Partial Trajectory Observations

Abstract

Tracking the movements of a target based on limited observations plays a role in many interesting applications. Existing probabilistic tracking techniques have shown considerable success but the majority assume simplistic motion models suitable for short-term, local motion prediction. Agent movements are often governed by more sophisticated mechanisms such as a goal-directed path-planning algorithm. In such contexts we must go beyond estimating a target's current location to consider its future path and ultimate goal. We show how to use complex, black box motion models to infer distributions over a target's current position, origin, and destination, using only limited observations of the full path. Our approach accommodates motion models defined over a graph, including complex pathing algorithms such as A*. Robust and practical inference is achieved by using hidden semi-Markov models (HSMMs) and graph abstraction. The method has also been extended to effectively track multiple, indistinguishable agents via a greedy heuristic.