| Title | Convolutional Neural Network based Foreground Segmentation for Video Surveillance Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Shahbaz, Ajmal, Hoang, Van-Thanh, Jo, Kang-Hyun |
| Conference Name | IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society |
| Keywords | Biological system modeling, Change detection, Change detection algorithms, CNN, Computer vision, constructed foreground mask, convolutional neural nets, convolutional neural network, dynamic backgrounds, Entropy, extracted feature maps, feature extraction, Foreground segmentation, foreground segmentation algorithm, foreground segmentation methods, ground truth, Human Behavior, illumination changes, image segmentation, image sequences, interpolation, Kernel, Metrics, object detection, pubcrawl, Resiliency, static foreground object, upsampled feature mask, video sequences, video surveillance, video surveillance system, video surveillance systems |
| Abstract | Convolutional Neural Networks (CNN) have shown astonishing results in the field of computer vision. This paper proposes a foreground segmentation algorithm based on CNN to tackle the practical challenges in the video surveillance system such as illumination changes, dynamic backgrounds, camouflage, and static foreground object, etc. The network is trained using the input of image sequences with respective ground-truth. The algorithm employs a CNN called VGG-16 to extract features from the input. The extracted feature maps are upsampled using a bilinear interpolation. The upsampled feature mask is passed through a sigmoid function and threshold to get the foreground mask. Binary cross entropy is used as the error function to compare the constructed foreground mask with the ground truth. The proposed algorithm was tested on two standard datasets and showed superior performance as compared to the top-ranked foreground segmentation methods. |
| DOI | 10.1109/IECON.2019.8927776 |
| Citation Key | shahbaz_convolutional_2019 |
Convolutional Neural Network based Foreground Segmentation for Video Surveillance Systems