Sequential image processing methods for improving semantic video segmentation algorithms

TL;DR

This paper introduces two sequential probabilistic methods that leverage temporal information from previous video frames to enhance the accuracy and consistency of semantic video segmentation algorithms, particularly for autonomous driving.

Contribution

It proposes novel sequential probabilistic approaches that improve existing semantic video segmentation algorithms by utilizing temporal dependencies between frames.

Findings

Increased segmentation accuracy using past frame information

Enhanced temporal consistency in segmentation results

Improved performance of state-of-the-art algorithms

Abstract

Recently, semantic video segmentation gained high attention especially for supporting autonomous driving systems. Deep learning methods made it possible to implement real time segmentation and object identification algorithms on videos. However, most of the available approaches process each video frame independently disregarding their sequential relation in time. Therefore their results suddenly miss some of the object segments in some of the frames even if they were detected properly in the earlier frames. Herein we propose two sequential probabilistic video frame analysis approaches to improve the segmentation performance of the existing algorithms. Our experiments show that using the information of the past frames we increase the performance and consistency of the state of the art algorithms.