### Abstract

An approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given measurement Z and two hypothesis H
_{1} and H
_{0}, we first estimate posterior probabilities P(H
_{0}|Z) and P(H
_{1}|Z); and choose the one with the larger posterior probability as the true hypothesis. Applications of the approach are illustrated with experiments devised to evaluate the performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented.

Original language | English (US) |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |

Publisher | IEEE |

Pages | 110-117 |

Number of pages | 8 |

Volume | 2 |

State | Published - 2000 |

Externally published | Yes |

Event | CVPR '2000: IEEE Conference on Computer Vision and Pattern Recognition - Hilton Head Island, SC, USA Duration: Jun 13 2000 → Jun 15 2000 |

### Other

Other | CVPR '2000: IEEE Conference on Computer Vision and Pattern Recognition |
---|---|

City | Hilton Head Island, SC, USA |

Period | 6/13/00 → 6/15/00 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Vision and Pattern Recognition
- Software
- Control and Systems Engineering
- Electrical and Electronic Engineering

### Cite this

*Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition*(Vol. 2, pp. 110-117). IEEE.

**Simultaneous tracking and verification via sequential posterior estimation.** / Li, Baoxin; Chellappa, Rama.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.*vol. 2, IEEE, pp. 110-117, CVPR '2000: IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA, 6/13/00.

}

TY - GEN

T1 - Simultaneous tracking and verification via sequential posterior estimation

AU - Li, Baoxin

AU - Chellappa, Rama

PY - 2000

Y1 - 2000

N2 - An approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given measurement Z and two hypothesis H 1 and H 0, we first estimate posterior probabilities P(H 0|Z) and P(H 1|Z); and choose the one with the larger posterior probability as the true hypothesis. Applications of the approach are illustrated with experiments devised to evaluate the performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented.

AB - An approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given measurement Z and two hypothesis H 1 and H 0, we first estimate posterior probabilities P(H 0|Z) and P(H 1|Z); and choose the one with the larger posterior probability as the true hypothesis. Applications of the approach are illustrated with experiments devised to evaluate the performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented.

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M3 - Conference contribution

VL - 2

SP - 110

EP - 117

BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

PB - IEEE

ER -