Multi-agent activity recognition using observation decomposedhidden Markov models
| Autor: | Xiao-Hui Liu, Chin-Seng Chua |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
business.industry
Computer science Feature vector Variable-order Markov model Pattern recognition Markov model computer.software_genre Task (project management) Activity recognition Set (abstract data type) Signal Processing Computer Vision and Pattern Recognition Data mining Artificial intelligence business Hidden Markov model computer Curse of dimensionality |
| Zdroj: | Image and Vision Computing. 24:166-175 |
| ISSN: | 0262-8856 |
| DOI: | 10.1016/j.imavis.2005.09.024 |
| Popis: | To automatically recognize multi-agent activities is a highly challenging task due to the complexity of the interactions between agents. The difficulties in this task stem from two aspects: firstly, the feature vectors derived from input data are of large dimensionality and variable length. Secondly, an efficient mapping of agents from input data to pre-defined activity models, known as agent assignment, is required. This paper presents a new method to model and classify multi-agent activities based on the proposed observation decomposed hidden Markov models (ODHMMs). To handle the feature vectors, we decomposed each original feature vector into a set of sub-feature vectors to keep the explored feature space consistent. Agent assignment is realized using a newly introduced parameter, which represents the 'role' of each agent. The experimental results show that the proposed method can successfully classify three-person activities with high accuracy and is less sensitive to incomplete data input. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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