Foreground Objects Detection using a Fully Convolutional Network with a Background Model Image and Multiple Original Images

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Kim, Jae-Yeul, Ha, Jong-Eun
• Format: Article
• Language: English
• Subjects: Algorithms; Camouflage; Classification algorithms; Datasets; Deep learning; Feature extraction; Foreground objects detection; fully convolutional network; generalization power; Image segmentation; Machine learning; Motion perception; Object detection; Object motion; Object recognition; Retraining; Surveillance; Training; Vibration; visual surveillance; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3020818

Visual surveillance aims to reliably extract foreground objects. Traditional algorithms usually use a background model image which is generated through the probabilistic modeling of changes over time and space. They detect foreground objects by comparing a background model image with a current image. Hard shadows, illumination changes, camouflage, camera jitter, and ghost object motion make the robust detection of foreground objects difficult in visual surveillance. Recently, various methods based on deep learning have been applied to visual surveillance. It has been shown that deep learning approaches can stably extract salient features, and they give a superior result compared to traditional algorithms. However, they show a good performance only for scenes that are similar to a scene used in training. Without retraining on a new scene, they give a worse result compared to traditional algorithms. In this paper, we propose a stable foreground object detection algorithm through the integration of a background model image used in traditional methods and deep learning methods. A background model image generated by SuBSENSE and multiple images are used as the input of a fully convolutional network. Also, it is shown that it is possible to improve a generalization power by training the proposed network using diverse scenes from an open dataset. We show that the proposed algorithm can have a superior result compared to deep learning-based and traditional algorithms in a new scene without retraining the network. The performance of the proposed algorithm is evaluated using various datasets such as the CDnet 2014, SBI, LASIESTA, and our own datasets. The proposed algorithm shows improvement of 17.5%, 8.9%, and 4.3%, respectively, in FM score compared to three deep learning-based algorithms.