Name
Abstract | ||
|---|---|---|
To understand the behavior of moving entities in a given environment, one should be capable of predicting their motion, that is, to model their dynamics. In a setting where different behaviors can arise, one can assume that each of them corresponds to different motivational states of observed entities. Here, those motivations are understood as goal positions or spots where entities seek to arrive. To build prediction models based on that idea, we present an unsupervised method to estimate motivational spots actively. Additionally, we use the output of such process to refine an adaptive system modeling the dynamics of inferred hidden causes of observed data. The whole method uses deep variational methods, and particularly, the network estimating motivations is trained through dynamic programming. Results show that modeling the dynamics of entities can be better achieved by integrating information about motivational spots. Notably, a network modeling the dynamics converges faster through the incorporation of information about motivations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/AVSS.2017.8078524 | 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
Keywords | Field | DocType |
active estimation,motivational spots,deep variational methods,dynamic programming,dynamic interaction modeling,adaptive system modeling | Dynamic programming,Computer science,Adaptive system,Artificial intelligence,Predictive modelling,Artificial neural network,Machine learning,Network model,Encoding (memory) | Conference |
ISBN | Citations | PageRank |
978-1-5386-2940-6 | 1 | 0.39 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Sebastian Olier | 1 | 1 | 0.39 |
| Damian Campo | 2 | 16 | 6.41 |
| Lucio Marcenaro | 3 | 401 | 66.21 |
| Emilia I. Barakova | 4 | 212 | 32.26 |
| Matthias Rauterberg | 5 | 1212 | 209.22 |
| Carlo S. Regazzoni | 6 | 609 | 101.09 |