A Review of Foreground Segmentation based on Convolutional Neural Networks
Pavan Kumar Tadiparthi, Sagarika Bugatha, Pradeep Kumar Bheemavarapu · International Journal of Advanced Computer Science and Applications · 2022
Foreground segmentation in dynamic videos is a challenging task for many researchers. Many researchers worked on various methods that were traditionally developed; however, the performance of those state-of-art procedures has not yielded encouraging results. Hence, to obtain efficient results, a deep learning-based neural network model is proposed in this paper. The proposed methodology is based on Convolutional Neural Network (CNN) model incorporated with Visual Geometry Group (VGG) 16 architecture, which is further divided into two sections, namely, Convolutional Neural Network section for feature extraction and Transposed Convolutional Neural Network (TCNN) section for un-sampling feature maps. Then the thresholding technique is employed for effective segmentation of foreground from background in images. The Change Detection (CDNET) 2014 benchmark dataset is used for the experimentation. It consists of 11 categories, and each category contains four to six videos. The baseline, camera jitter, dynamic background, and bad weather are the categories considered for the experimentation. The performance of the proposed model is compared with the state-of-the-art techniques, such as Gaussian Mixture Model (GMM) and Visual Background Extractor (VIBE) for its efficiency in segmenting foreground images.