Background Subtraction Based on Encoder-Decoder Structured CNN

Abstract

Background subtraction is commonly adopted for detecting moving objects in image sequence. It is an important and fundamental computer vision task and has a wide range of applications. We propose a background subtraction framework with deep learning model. Pixels are labeled as background or foreground by an Encoder-Decoder Structured Convolutional Neural Network (CNN). The encoder part produces a high-level feature vector. Then, the decoder part uses the feature vector to generate a binary segmentation map, which can be used to identify moving objects. The background model is generated from the image sequence. Each frame of the image sequence and the background model are input to the CNN for pixel classification. Background subtraction result can be erroneous as videos may be captured in various complex scenes. The background model must be updated. Therefore, we propose a feedback scheme to perform the pixelwise background model updating. For the training of the CNN, the input images and the corresponding ground truths are drawn from the benchmark dataset Change Detection 2014. The results show that our proposed architecture outperforms many well-known traditional and deep learning background subtraction algorithms.