Automatically assigned DDC number: 00637

Manually assigned DDC number: 00637

Number of references: 8

Title: Joint Probabilistic Techniques for Tracking Objects Using Multiple Visual Cues

Author:

Author:

Subject: Christopher Rasmussen,Gregory D. Hager Joint Probabilistic Techniques for Tracking Objects Using Multiple Visual Cues

Description: Autonomous robots relying on vision as a primary sensor frequently must identify and track common objects such as people and cars in order to successfully perform navigation, interaction, and grasping tasks. These objects 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 cues such as color and shape are deployed simultaneously. The utility of each cue varies according to image conditions, necessitating adaptation in the weighting of the various methods to avoid bias. This is achieved by formulating a measure of tracker confidence based on distinctiveness and occlusion probability, which perm...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1998-02-18

Pubyear: 1998

Format: ps

Identifier: http://citeseer.ist.psu.edu/180261.html

Source: http://www.cs.yale.edu/HTML/YALE/CS/HyPlans/rasmussen/lib/papers/iros98.ps.gz

Language: en

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Rights: unrestricted

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