Video Segmentation Learning Using Cascade Residual Convolutional Neural Network

TL;DR

This paper introduces a novel deep learning method using cascade residual convolutional neural networks for accurate video segmentation, effectively handling challenging conditions like weather changes and shadows.

Contribution

The proposed approach incorporates residual information into foreground detection, achieving top-tier accuracy with significantly fewer parameters than existing methods.

Findings

Achieved F-measures of 0.9535 and 0.9636 on two datasets.

Placed among the top 3 state-of-the-art techniques.

Uses approximately seven times fewer parameters than the leading method.

Abstract

Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of $\mathbf{0.9535}$ and $\mathbf{0.9636}$ in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.