A probabilistic graph-based framework for plug-and-play multi-cue visual tracking
In this paper, we propose a novel approach for integrating multiple tracking cues within a unified probabilistic graph-based Markov random fields (MRFs) representation. We show how to integrate temporal and spatial cues encoded by unary and pairwise probabilistic potentials. As the inference of such...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 5 vom: 16. Mai, Seite 2291-301 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In this paper, we propose a novel approach for integrating multiple tracking cues within a unified probabilistic graph-based Markov random fields (MRFs) representation. We show how to integrate temporal and spatial cues encoded by unary and pairwise probabilistic potentials. As the inference of such high-order MRF models is known to be NP-hard, we propose an efficient spectral relaxation-based inference scheme. The proposed scheme is exemplified by applying it to a mixture of five tracking cues, and is shown to be applicable to wider sets of cues. This paves the way for a modular plug-and-play tracking framework that can be easily adapted to diverse tracking scenarios. The proposed scheme is experimentally shown to compare favorably with contemporary state-of-the-art schemes, and provides accurate tracking results |
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| Beschreibung: | Date Completed 30.03.2015 Date Revised 27.10.2019 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |