Deep Variation Transformation Network for Foreground Detection

In existing literature, the distribution of pixel observations is analyzed with models designed for the video foreground detection task. However, it is possible that the background and foreground share similar observations, causing false detections. We propose a novel foreground detection method called Deep Variation Transformation Network (DVTN), focusing on analyzing the pixel variations instead of distributions. In particular, pixel variations are represented by a sequence of pixel observations, and DVTN is trained to transform the pixel variations into a new space, where the observations can be classified easily. Following this, the output of DVTN is utilized by a linear classifier to label pixels as foreground or background. As a result of the global analysis and the strong learning ability of DVTN, the proposed approach adaptively learns a good transformation from pixel variations to probabilities of labels to improve performance. Comprehensive experiments on several benchmark datasets demonstrate the superiority of our DVTN approach compared to both state-of-the-art deep learning and traditional methods, especially in scenes lacking texture and color information. Code is available at https://github.com/Zhangjunyin/DVTN .