Abstract:
Moving object segmentation in videos (MOS) is a highly
demanding task for security-based applications like automated outdoor video surveillance. Most of the existing techniques proposed for MOS are highly depend on fine-tuning
a model on the first frame(s) of test sequence or complicated
training procedure, which leads to limited practical serviceability of the algorithm. In this paper, the inherent correlation learning-based edge extraction mechanism (EEM) and
dense residual block (DRB) are proposed for the discriminative foreground representation. The multi-scale EEM
module provides the efficient foreground edge related information (with the help of encoder) to the decoder through
skip connection at subsequent scale. Further, the response
of the optical flow encoder stream and the last EEM module
are embedded in the bridge network. The bridge network
comprises of multi-scale residual blocks with dense connections to learn the effective and efficient foreground relevant
features. Finally, to generate accurate and consistent foreground object maps, a decoder block is proposed with skip
connections from respective multi-scale EEM module feature maps and the subsequent down-sampled response of
previous frame output. Specifically, the proposed network
does not require any pre-trained models or fine-tuning of
the parameters with the initial frame(s) of the test video.
The performance of the proposed network is evaluated with
different configurations like disjoint, cross-data, and global
training-testing techniques. The ablation study is conducted
to analyse each model of the proposed network. To demonstrate the effectiveness of the proposed framework, a comprehensive analysis on four benchmark video datasets is
conducted. Experimental results show that the proposed approach outperforms the state-of-the-art methods for MOS
