Background Subtraction Using Encoder-Decoder Structured Convolutional Neural Network

A background subtraction algorithm using an encoder decoder structured convolutional neural network is proposed in this work, in order to segment out moving objects from the background. A target frame, its previous frame, and a background model are concatenated and fed into the network as the input. Then, the encoder generates a high-level feature vector, and the decoder converts the feature vector into a segmentation map, which roughly identifies moving object regions. Moreover, we develop background modeling and foreground extraction techniques, which exploit contour information. Experimental results on the CDnet2014 dataset demonstrate that the proposed algorithm outperforms state-of-the-art techniques significantly.