Latent topic model-based group activity discovery
| Autor: | Tanveer A. Faruquie, Prem Kalra, Subhashis Banerjee |
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| Rok vydání: | 2011 |
| Předmět: |
Topic model
Group (mathematics) Computer science business.industry Latent variable Machine learning computer.software_genre Computer Graphics and Computer-Aided Design Latent Dirichlet allocation Correlation symbols.namesake Salient symbols Multinomial distribution Computer Vision and Pattern Recognition Artificial intelligence business computer Software Gibbs sampling |
| Zdroj: | The Visual Computer. 27:1071-1082 |
| ISSN: | 1432-2315 0178-2789 |
| DOI: | 10.1007/s00371-011-0652-1 |
| Popis: | Surveillance videos of public places often consist of group activities composed from multiple co-occurring individual activities. However, latent topic models, such as Latent Dirichlet Allocation (LDA), which have been successfully used to discover individual activities, do not discover group activities. In this paper we propose a method to discover group activities along with individual activities. We use a two layer latent structure where a latent variable is used to discover correlation of individual activities as a group activity using multinomial distribution. Each individual activity is in turn represented as a distribution over local visual features. We use a Gibbs sampling-based algorithm to jointly infer the individual and group activities. Our method can summarize not only the individual activities but also the common group activities in a video. We demonstrate the strength of our method by discovering activities and the salient correlation amongst them in real life videos of crowded public places. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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