Background subtraction is an important topic in computer vision and video analysis. It is challenging to robustly segment foreground and background in complex scenarios. In the literature there are efforts to address some of the main challenges such as illumination change, dynamic backgrounds, hard shadows, and intermittent object motion. However, most of the research has focused on applying advanced mathematical and machine learning models rather than on improving performance in real-time applications. In this paper, we devise a method named EGMM to efficiently handle the illumination change problem and also operate at a real-time execution speed on commodity PC hardware. EGMM is an ensemble algorithm that fuses multiple Gaussian Mixture Models operating on gradient, texture and color features. Detection and removal of shadows is done using a chromaticity-based approach, and spatio-temporal history of foreground blobs is used to handle intermittent object motion. We benchmarked EGMM by creating datasets for two light change scenarios. The results demonstrate that EGMM achieves robust performance in complex illumination change cases, outperforms some state-of-the-art algorithms, and runs at 100 fps (GPU) at$1280\times720$resolution. Moreover, experiments using the 2012 CDnet dataset show that EGMM achieves generally good performance in varying scenes with overall results better than conventional methods and runs at 1000 fps (GPU) at$320\times 240$resolution.

Learning similarity of two images is an important problem in computer vision and has many potential applications. Most of previous works focus on generating image similarities in three aspects: global feature distance computing, local feature matching and image concepts comparison. However, the task of directly detecting class agnostic common objects from two images has not been studied before, which goes one step further to capture image similarities at region level. In this paper, we propose an end-to-end Image Common Object Detection Network (CODN) to detect class agnostic common objects from two images. The proposed method consists of two main modules: locating module and matching module. The locating module generates candidate proposals of each two images. The matching module learns the similarities of the candidate proposal pairs from two images, and refines the bounding boxes of the candidate proposals. The learning procedure of CODN is implemented in an integrated way and a multi-task loss is designed to guarantee both region localization and common object matching. Experiments are conducted on PASCAL VOC 2007 and COCO 2014 datasets. Experimental results validate the effectiveness of the proposed method.