Using thermal intensities to build conditional random fields for object segmentation at night
Arnald Dutta, Bodhisatwa Mandal, Swarnendu Ghosh, Nibaran Das · 2020
Surveillance systems often make use of the thermal spectrum for improved computer vision application during night time scenarios. In the proposed work we tackle a similar problem of street object segmentation using multispectral information. We propose an efficient model that utilizes spark modules to reduce network parameters and perform a multispectral feature extraction along with multi-scale feature fusion in an encoder-decoder architecture. The predictions are further refined using a conditional random field based on thermal intensity-based pairwise potentials. Overall the network achieves an average pixel-level accuracy of 0.8185 and a mean IOU of 0.6438 for the night dataset which exceeds competing models by a significant margin. The proposed method also performs well for the combined dataset of day and night images. Additionally, it has been established that the use of thermal intensity-based pairwise potential on average is superior when it comes to images captured during the night.