Abstract
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 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 focusing on color-region- and snake-based tracking that demonstrate the efficacy of this approach.
Original language | English (US) |
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Pages (from-to) | 16-21 |
Number of pages | 6 |
Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
State | Published - Dec 1 1998 |
Externally published | Yes |
Event | Proceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA Duration: Jun 23 1998 → Jun 25 1998 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition