A novel deep convolutional encoder-decoder network: application to moving object detection in videos

Moving object detection is one of the key applications of video surveillance. Deep convolutional neural networks have gained increasing attention in the field of video surveillance due to their effective feature learning ability. The performance of deep neural networks is often affected by the characteristics of videos like poor illumination and inclement weather conditions. It is important to design an innovative architecture of deep neural networks to deal with the videos effectively. Here, the convolutional layers for the networks require to be in an appropriate number and it's important to determine the number. In this study, we propose a customized deep convolutional encoder-decoder network, say CEDSegNet, for moving object detection in a video sequence. Here, the CEDSegNet is based on SegNet, and its encoder and decoder parts are chosen to be two. By customizing the SegNet with two encoder and decoder parts, the proposed CEDSegNet improves detection performance, where its parameters are reduced to an extent. The two encoder and decoder parts function towards generating feature maps preserving the fine details of object pixels in videos. The proposed CEDSegNet is tested on multiple video sequences of the CDNet dataset2012. The results obtained using CEDSegNet for moving object detection in the video frames are interpreted qualitatively. Further, the performance of CEDSegNet is evaluated using several quantitative indices. Both the qualitative and quantitative results demonstrate that the performance of CEDSegNet is superior to the state-of-the-network models, VGG16, VGG19, ResNet18 and ResNet50.