Gibbs Sampling Strategies for Semantic Perception of Streaming Video Data
This work addresses real-time semantic perception for mobile robots, but it is incremental as it builds on prior online topic modeling techniques.
The paper tackled the problem of topic modeling for streaming video data from mobile robots by comparing Gibbs sampling strategies, resulting in lower online and final perplexity compared to existing methods like o-LDA and incremental LDA.
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.