Foreground Detection in Surveillance Video with Fully Convolutional Semantic Network

Abstract

Foreground detection is an important part of surveillance video analysis, and also has challenges. For example, the classical methods are difficult to distinguish the foreground, which is similar to the background. In recent years, Convolutional Neural Networks (CNNs) have been widely used in image processing and achieved better performance. In this paper, we proposed an efficient deep Fully Convolutional Semantic Networks (FCSN) model for foreground detection in surveillance video. Our model aimed at learning the global differences between the video frame and the background image, and the semantic information by utilizing the pre-trained weights on semantic segmentation. In the experiment, unlike other related work, we proposed a reasonable method, which is able to avoid overfitting results to construct training data with 20 videos and test data with 6 videos on the dataset of 2014 ChangeDetection.net (CDnet 2014). Experimental results verified that our model outperforms the state-of-the-art methods in the foreground detection of surveillance video.