Joint Probabilistic Techniques for Tracking Multi-Part Objects

Common objects such as people and cars 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 combining and sharing information among several 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 disparate visual cues are deployed simultaneously. We also formulate a measure of tracker con dence, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble. We will discuss experiments focusing on color-region- and snake-based tracking that demonstrate the e cacy of this approach.

• Type: Conference
• Year: 1998
• Where: CVPR
• Authors: Christopher Rasmussen, Gregory D. Hager
• Keywords: Computer Vision, CVPR 1998, Erroneous State Estimates, Image-based Tracking Algorithms, Joint Probabilistic Data, Underlying Visual Object, Visual Cues