Using Stacked Sparse Auto-Encoder and Superpixel CRF for Long-Term Visual Scene Understanding of UGVs

Multiple images have been widely used for scene understanding and navigation of unmanned ground vehicles in long term operations. However, as the amount of visual data in multiple images is huge, the cumulative error in many cases becomes untenable. This paper proposes a novel method that can extract features from a large dataset of multiple images efficiently. Then the membership
${K}$
-means clustering is used for high dimensional features, and the large dataset is divided into
${N}$
subdatasets to train
${N}$
conditional random field (CRF) models based on superpixel. A Softmax subdataset selector is used to decide which one of the
${N}$
CRF models is chosen as the prediction model for labeling images. Furthermore, some experiments are conducted to evaluate the feasibility and performance of the proposed approach.