Multiple Target Tracking by Integrating Track Refinement and Data Association

Abstract

Multiple target tracking that integrates target model estimation and data association steps is described. The integration allows successive refinement of the models while reducing the uncertainty in data association. Each target is described by ”weak” models of kinematics, shape and appearance. The target models are refined in a two-stage process: image-based tracklets of high purity and accuracy are generated, and geospatial tracks are extended from these tracklets. During each stage of tracking, observation data of reduced uncertainties are associated with the refined tracks in a probabilistic manner. We describe our approach in the context of a real time system that has been tested and evaluated for vehicle and human tracking in sparse, medium, and dense clutter using aerial EO/IR video.