| Autor: |
Faruquie, Tanveer A., Banerjee, Subhashis, Kalra, Prem K. |
| Zdroj: |
2012 IEEE Ninth International Conference on Advanced Video & Signal-Based Surveillance; 1/ 1/2012, p100-105, 6p |
| Abstrakt: |
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. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
