Unsupervised Discovery of Activities and Their Temporal Behaviour
| Autor: | Tanveer A. Faruquie, Subhashis Banerjee, Prem Kalra |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Machine learning computer.software_genre Motion (physics) Visualization Generative model Probability distribution Mixture distribution Multinomial distribution Gibbs sampling algorithm Artificial intelligence Data mining Hidden Markov model business computer |
| Zdroj: | AVSS |
| DOI: | 10.1109/avss.2012.79 |
| Popis: | This paper addresses the problem of discovering activities and their temporal significance in surveillance videos in an unsupervised manner. We propose a generative model that can jointly capture the activities and their behaviour over time. We use multinomial distribution over local motion features to model activities and a mixture distribution over their time stamps to capture the multi-modal temporal distribution of these activities. We give a Gibbs sampling algorithm to infer the parameters of the model. We demonstrate the effectiveness of our approach on real life surveillance feed of outdoor scenes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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