Moving Objects Detection with a Moving Camera: A Comprehensive Review

Abstract

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.

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

“…Many notable surveys related to change detection have been published, as summarized in Table I. These include many excellent surveys of the traditional background subtraction methods [36], [37], [42], [45], [47], deep neural network methods for background subtraction [48], traffic monitoring [38], [50], background initialization [43], foreground detection [40], wide area motion detection [39], maritime surveillance [41], [44], and moving object detection with a moving camera [46], [49].…”

Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment

“…Motion Boundaries -Previous works, such as [27,7,6], have shown the importance of using optical flow in deep learning-based human action recognition. However, optical flow fields represent the absolute motion, making the disentanglement of object-level and camera motions a significant challenge [32]. [7] proposed to use warped flow [33] to cancel out the camera motion.…”