Background modeling for moving object detection in long-distance imaging through turbulent medium

Abstract

A basic step in automatic moving objects detection is often modeling the background (i.e., the scene excluding the moving objects). The background model describes the temporal intensity distribution expected at different image locations. Long-distance imaging through atmospheric turbulent medium is affected mainly by blur and spatiotemporal movements in the image, which have contradicting effects on the temporal intensity distribution, mainly at edge locations. This paper addresses this modeling problem theoretically, and experimentally, for various long-distance imaging conditions. Results show that a unimodal distribution is usually a more appropriate model. However, if image deblurring is performed, a multimodal modeling might be more appropriate.

LRSDSFD: low-rank sparse decomposition and symmetrical frame difference method for moving video foreground-background separation

“…At present, the mainstream algorithms for moving video foreground and background separation can be divided into optical flow method [5], frame difference method [6] and background modeling method [7][8]. Low rank sparse decomposition model is one of the background modeling methods.…”

Detecting and tracking moving objects in long-distance imaging through turbulent medium

“…The wide gray level range in the background model (i.e., the temporal statistical behavior of a pixel) stems from the turbulence-based image movements and on the local image structure, while the shape of the model depends also on the blur effect [6].…”