An End to End Encoder-Decoder Network with Multi-scale Feature Pulling for Detecting Local Changes From Video Scene
Manoj Kumar Panda, Badri Narayan Subudhi, Thierry Bouwmans, Vinit Jakheytiya, T. Veerakumar
Abstract
Local change detection for moving object detection is an essential step in any computer vision task. The most well-known technique is background subtraction BGS. However, the performance of BGS is strongly dependent on the background construction. The background construction to be robust in the presence of various challenges: dynamic backgrounds, illumination changes, camera jitter, etc. In this paper, we propose a novel encoder-decoder-based end-to-end deep learning framework for BGS. Thus, we explore a VGG-19 deep network with a transfer learning strategy as an encoder that deeply learned and extracted the features at different levels. We herewith propose a Multi-scale Feature Pulling MFP block which can retain the features at the various scales of the challenging video scenes. We also design a decoder network which is a stack of several transposed convolutional layers which precisely predict that each pixel of the target frame belongs to the background or foreground. The efficiency of the proposed algorithm is validated on the CDNet-2014 dataset by comparing its results against seventeen state-of-the-art techniques.