Moving Objects Detection with a Moving Camera: A Comprehensive Review
It provides a comprehensive survey for researchers in computer vision, but is incremental as it organizes existing work without new results.
This paper reviews methods for detecting moving objects with moving cameras, categorizing them by scene representation and eight approaches, and also covers datasets and evaluation metrics.
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify and categorize the different existing methods found in the literature. For this purpose, we propose to classify these methods according to the choose of the scene representation: one plane or several parts. Inside these two categories, the methods are grouped according to eight different approaches: panoramic background subtraction, dual cameras, motion compensation, subspace segmentation, motion segmentation, plane+parallax, multi planes and split image in blocks. A reminder of methods for static cameras is provided as well as the challenges with both static and moving cameras. Publicly available datasets and evaluation metrics are also surveyed in this paper.