Topic modeling of streaming sensor data can be used for high level perception of the environment by a mobile robot. In this paper we compare various Gibbs sampling strategies for topic modeling of streaming spatiotemporal data, such as video captured by a mobile robot. Compared to previous work on online topic modeling, such as o-LDA and incremental LDA, we show that the proposed technique results in lower online and final perplexity, given the realtime constraints.
@article{arxiv.1509.03242,
title = {Gibbs Sampling Strategies for Semantic Perception of Streaming Video Data},
author = {Yogesh Girdhar and Gregory Dudek},
journal= {arXiv preprint arXiv:1509.03242},
year = {2015}
}