A Survey of Efficient Deep Learning Models for Moving Object Segmentation

Abstract

Moving object segmentation (MOS) is the process of identifying dynamic objects from video frames, such as moving vehicles or pedestrians, while discarding the background. It plays an essential role in many real-world applications such as autonomous driving, mobile robots, and surveillance systems. With the availability of a huge amount of data and the development of powerful computing infrastructure, deep learning-based methods have shown remarkable improvements in MOS tasks. However, as the dimension of data becomes higher and the network architecture becomes more complicated, deep learning-based MOS models are computationally intensive, which limits their deployment on resource-constrained devices and in delay-sensitive applications. Therefore, more research started to develop fast and lightweight models. This paper aims to provide a comprehensive review of deep learning-based MOS models, with a focus on efficient model design techniques. We summarize a variety of MOS datasets, and conduct a thorough review of segmentation accuracy metrics and model efficiency metrics. Most importantly, we compare the performance of efficient MOS models on popular datasets, identify competitive models and analyze their essential techniques. Finally, we point out existing challenges and present future research directions.