Foreground Objects Detection by U-Net with Multiple Difference Images

• Published in: Applied sciences 2021-02, Vol.11 (4), p.1807
• Main Authors: Kim, Jae-Yeul, Ha, Jong-Eun
• Format: Article
• Language: English
• Subjects: Algorithms; Datasets; Deep learning; Mathematical models; Neural networks; object detection; Principal components analysis; Statistical methods; Surveillance; visual surveillance
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11041807

In video surveillance, robust detection of foreground objects is usually done by subtracting a background model from the current image. Most traditional approaches use a statistical method to model the background image. Recently, deep learning has also been widely used to detect foreground objects in video surveillance. It shows dramatic improvement compared to the traditional approaches. It is trained through supervised learning, which requires training samples with pixel-level assignment. It requires a huge amount of time and is high cost, while traditional algorithms operate unsupervised and do not require training samples. Additionally, deep learning-based algorithms lack generalization power. They operate well on scenes that are similar to the training conditions, but they do not operate well on scenes that deviate from the training conditions. In this paper, we present a new method to detect foreground objects in video surveillance using multiple difference images as the input of convolutional neural networks, which guarantees improved generalization power compared to current deep learning-based methods. First, we adjust U-Net to use multiple difference images as input. Second, we show that training using all scenes in the CDnet 2014 dataset can improve the generalization power. Hyper-parameters such as the number of difference images and the interval between images in difference image computation are chosen by analyzing experimental results. We demonstrate that the proposed algorithm achieves improved performance in scenes that are not used in training compared to state-of-the-art deep learning and traditional unsupervised algorithms. Diverse experiments using various open datasets and real images show the feasibility of the proposed method.