Joint probabilistic techniques for tracking objects using multiple visual cues

Robots relying on vision as a primary sensor frequently need to track common objects such as people, cars, and tools in order to successfully perform autonomous navigation or grasping tasks. These objects may comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying characteristics. In this paper, we present a framework for sharing information among disparate state estimation processes operating on the same underlying visual object. Well-known techniques for joint probabilistic data association are adapted to yield increased robustness when multiple trackers attuned to different visual cues are deployed simultaneously. We also formulate a measure of tracker confidence, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble. We will discuss experiments using color-region, and snake-based tracking in tandem that demonstrate the efficacy of this approach.