Video monitoring system employing hierarchical hidden markov model (HMM) event learning and classification
US7076102B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 27, 2002 |
| Grant date | Jul 11, 2006 |
| Priority date | — |
| Expiry date | Jun 8, 2024 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20081
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A method and apparatus are disclosed for automatically learning and identifying events in image data using hierarchical HMMs to define and detect one or more events. The hierarchical HMMs include multiple paths that encompass variations of the same event. Hierarchical HMMs provide a framework for defining events that may be exhibited in various ways. Each event is modeled in the hierarchical HMM with a set of sequential states that describe the paths in a high-dimensional feature space. These models can then be used to analyze video sequences to segment and recognize each individual event to be recognized. The hierarchical HMM is generated during a training phase, by processing a number of images of the event of interest in various ways, typically observed from multiple viewpoints.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.