A Multi-Scale Contrast Preserving Encoder-Decoder Architecture for Local Change Detection From Thermal Video Scenes

This article presents a new deep-learning architecture based on an encoder-decoder framework that retains contrast while performing background subtraction (BS) on thermal videos. The proposed scheme consists of three consecutive blocks: the encoder, the Multi-Scale Contrast Preservation (MSCP) block, and the decoder. The encoder network employs a hybrid of convolution and atrous convolution blocks to preserve both sparse and dense features, with a skip connection. The encoder, combined with the MSCP block, maintains multi-scale contrast features with reduced training loss. Furthermore, the decoder network accurately projects the extracted features at different layers into pixel-level detail. The proposed end-to-end model efficiently provides a binary map for the corresponding thermal video scene. The efficiency of the proposed algorithm is validated on two large-scale datasets, namely CDnet 2014 and the Tripura University Video Dataset at Night Time (TU-VDN). Both qualitative and quantitative results demonstrate that MSCP outperforms thirty-eight existing BS schemes.