2017/02/06 by Mohammadreza Babaee, Babaee, Mohammadreza, Duc Tung Dinh +3 · 54 citations
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial intelligence #Background subtraction #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Convolutional neural network #Deep learning #FOS: Computer and information sciences #Feature (linguistics) #Ground truth #Network architecture #Pattern recognition (psychology) #Pixel #Remote-Sensing Image Classification #Segmentation #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1702.01731
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/02/06 · openalex publication_date 2017/02/06 · arxiv updated 2017/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In this work, we present a novel background subtraction system that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With this approach, feature engineering and parameter tuning become unnecessary since the network parameters can be learned from data by training a single CNN that can handle various video scenes. Additionally, we propose a new approach to estimate background model from video. For the training of the CNN, we employed randomly 5 percent video frames and their ground truth segmentations taken from the Change Detection challenge 2014(CDnet 2014). We also utilized spatial-median filtering as the post-processing of the network outputs. Our method is evaluated with different data-sets, and the network outperforms the existing algorithms with respect to the average ranking over different evaluation metrics. Furthermore, due to the network architecture, our CNN is capable of real time processing.