Sequential image processing methods for improving semantic video segmentation algorithms
This addresses a key issue for autonomous driving systems by enhancing segmentation reliability, though it is incremental as it builds on existing methods.
The paper tackles the problem of inconsistent object detection across frames in semantic video segmentation by proposing two sequential probabilistic video frame analysis approaches, which improve the performance and consistency of state-of-the-art algorithms.
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.