Name
Abstract | ||
|---|---|---|
Moving object detection is a crucial problem in computer vision. This affects the performance of the overall system in surveillance applications. In this paper, a Deep-Convolutional Neural Network with fully convolutional approach is proposed. Convolutional networks are powerful models to extract hierarchies of non-handcrafted features. The primary objective of the paper is to build an accurate foreground segmentation system with limited user interventions. The presented work focuses to build a fully convolutional network with skip architecture to identify moving objects in complex scenarios. The network is modeled as an end-to-end fully convolutional network, and the method contains a new hierarchical pooling layer to make use of global contextual information. The presented model utilizes a pre-trained VGG-19 Net model for the construction of Deep-Convolutional Neural Network (Deep-CNN) model. The fine and coarse features are fused using skip architecture to improve the feature representation. The qualitative and quantitative performance of the Deep-CNN architecture is tested on ChangeDetection.net-2014 dataset. The results produced by the Deep-CNN method were compared with the techniques in the recent literature. The Deep-CNN method outperforms the state-of-the-art methods without relying on any post-processing techniques. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-019-07800-0 | Multimedia Tools and Applications |
Keywords | DocType | Volume |
Deep-convolutional neural network, Foreground segmentation, Fully convolutional network, Hierarchical-pooling, Skip-architecture | Journal | 79 |
Issue | ISSN | Citations |
15 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Midhula Vijayan | 1 | 2 | 2.04 |
| Rakesh Mohan | 2 | 345 | 38.02 |
| Preeth Raguraman | 3 | 2 | 0.69 |