Location-specific Transition Distributions for Tracking

Abstract

Surveillance and tracking systems often observe the same scene over extended time periods. When object motion is constrained by the scene (for instance, cars on roads, or pedestrians on sidewalks), it is advantageous to characterize and use scene-specific and location-specific priors to aid the tracking algorithm. This paper develops and demonstrates a method for creating priors for tracking that are conditioned on the current location of the object in the scene. These priors can be naturally incorporated in a number of tracking algorithms to make tracking more efficient and more accurate. We present a novel method to sample from these priors and show performance improvements (in both efficiency and accuracy) for two different tracking algorithms in two different problem domains.