Bridging the Past, Present and Future: Modeling Scene Activities From Event Relationships and Global Rules
| Type of publication: | Conference paper |
| Citation: | Varadarajan_CVPR_2012 |
| Publication status: | Accepted |
| Booktitle: | IEEE Conference on Computer Vision and Pattern Recognition, 2012, Providence, Rhode Island, USA |
| Year: | 2012 |
| Month: | June |
| Abstract: | This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propose a novel topic model that accounts for the two main factors that affect these occurrences: (1) the existence of global scene states that regulate which of the activities can spontaneously occur; (2) local rules that link past activity occurrences to current ones with temporal lags. These complementary factors are mixed in the probabilistic generative process, thanks to the use of a binary random variable that selects for each activity occurrence which one of the above two factors is applicable. All model parameters are efficiently inferred using a collapsed Gibbs sampling inference scheme. Experiments on various datasets from the literature show that the model is able to capture temporal processes at multiple scales: the scene-level first order Markovian process, and causal relationships amongst activities that can be used to predict which activity can happen after another one, and after what delay, thus providing a rich interpretation of the scene’s dynamical content. |
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Idiap SNSF-MULTI VANAHEIM |
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| Added by: | [UNK] |
| Total mark: | 0 |
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