Cascaded Feature-Mask Fusion for Foreground Segmentation

IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 340–350

Abstract

Foreground segmentation aims at extracting moving objects from the background in a robust manner under various challenging scenarios. The deep learning-based methods have achieved remarkable improvement in this field. These methods produce semantically correct predictions based on extracted rich semantic features yet perform poorly on segmentation of edge details. The main reason is that the high-level features extracted by the deep network lose the high-frequency information for the successful edge segmentation. On this basis, we propose a novel segmentation network with a cascade architecture to refine segmentation results step by step by introducing detailed information into high-level features. The network recorrects and optimizes the segmentation maps in each step so that more accurate segmentation results are obtained. Furthermore, we evaluate our approach on the challenging CDnet2014 dataset and achieve an F-measure of 0.9868. Our approach thus outperforms previous methods, such as FgSegNet_v2, FgSegNet, BSPVGan, Cascade CNN, IUTIS-5, WeSamBE, DeepBS, and GMM-Stauffer.

Authors 6

1. Chuanyun Xu

   Chongqing Normal University · Chongqing University of Technology

   Affiliation as printed

   School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

   College of Computer and Information Science, Chongqing Normal University, Chongqing, China

2. Huan Liu

   Chongqing University of Technology

   Affiliation as printed

   School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

3. Tenghui Li Aachen Computer Science Department

   Chongqing University of Technology · RWTH Aachen University

   Affiliation as printed

   Computer Science Department, RWTH Aachen University, Aachen, Germany

   School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

4. Yang Zhang

   Chongqing Normal University

   Affiliation as printed

   College of Computer and Information Science, Chongqing Normal University, Chongqing, China

5. Li Tian Aachen Computer Science Department

   Chongqing University of Technology · RWTH Aachen University

   Affiliation as printed

   Computer Science Department, RWTH Aachen University, Aachen, Germany

   School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

6. Gang Li

   Chongqing University of Technology

   Affiliation as printed

   School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China