Abstract
The growth of the number of surveillance systems makes it is impossible to process data by human operators thereby autonomous algorithms are required in a decision-making procedure. A novel dynamic topic modeling approach for abnormal behaviour detection in video is proposed. Activities and behaviours in the scene are described by the topic model where temporal dynamics for behaviours is assumed. Here we implement Expectation-Maximisation algorithm for inference in the model and show in the experiments that it outperforms the Gibbs sampling inference scheme that is originally proposed in [1].
Original language | English |
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Title of host publication | The University of Sheffield Engineering Symposium |
Number of pages | 2 |
DOIs | |
Publication status | Published - 2015 |
Event | The University of Sheffield Engineering Symposium - Sheffield, UK United Kingdom Duration: 24 Jun 2015 → 24 Jun 2015 |
Other
Other | The University of Sheffield Engineering Symposium |
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Abbreviated title | USES 2015 |
Country/Territory | UK United Kingdom |
City | Sheffield |
Period | 24/06/15 → 24/06/15 |
Keywords
- Abnormal behaviour
- Computer vision
- Expectation-Maximisation algorithm
- Gibbs sampling
- Topic modeling