Deep Foreground Segmentation using Convolutional Neural Network

This paper proposes foreground segmentation algorithm powered by the convolutional neural network. The task requires CNN network to extract features from given image and upsample the image to segment background and foreground. The proposed algorithm consists of two networks. VGG-16 based CNN is used to extract the feature from the given image. The feature maps are upsampled using deconvolution network. The upsample image is segmented with sigmoid and threshold to get background foreground information. The proposed method is tested on all the categories of the change detection dataset. The dataset consists of 11 challenging categories such as dynamic background, bad weather, camera jitter, low frame rate, etc. The proposed method has been compared with state of the art foreground detection algorithms to prove effectiveness.