Automatic Video Object Segmentation (AVOS) refers to the task of autonomously segmenting target objects in video sequences without relying on human-provided annotations in the first frames. In AVOS, the use of motion information is crucial, with optical flow being a commonly employed method for capturing motion cues. However, the computation of optical flow is resource-intensive, making it unsuitable for real-time applications, especially on edge devices with limited computational resources. In this study, we propose using frame differences as an alternative to optical flow for motion cue extraction. We developed an extended U-Net-like AVOS model that takes a frame on which segmentation is performed and a frame difference as inputs, and outputs an estimated segmentation map. Our experimental results demonstrate that the proposed model achieves performance comparable to the model with optical flow as an input, particularly when applied to videos captured by stationary cameras. Our results suggest the usefulness of employing frame differences as motion cues in cases with limited computational resources.

本研究解决了自动视频目标分割（AVOS）中计算光流所需的高资源消耗问题。我们提出使用帧差异作为运动线索的替代方案，开发了一种扩展的U-Net架构，实验结果显示该模型在使用静态摄像机捕捉的视频中表现优于或接近传统光流方法，为资源有限的实时应用提供了一种有效的解决方案。

帧差异作为视频目标分割运动线索的研究