Surveillance in long-distance turbulence-degraded videos
Yitzhak Yitzhaky, Eli Chen, Oren Haik · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
Surveillance in long-distance turbulence-degraded video is a difficult challenge because of the effects of the atmospheric turbulence that causes blur and random shifts in the image. As imaging distances increase, the degradation effects become more significant. This paper presents a method for surveillance in long-distance turbulence-degraded videos. This method is based on employing new criteria for discriminating true from false object detections. We employ an adaptive thresholding procedure for background subtraction, and implement new criteria for distinguishing true from false moving objects, that take into account the temporal consistency of both shape and motion properties. Results show successful detection also tracking of moving objects on challenging video sequences, which are significantly distorted with atmospheric turbulence. However, when the imaging distance is increased higher false alarms may occur. The method presented here is relatively efficient and has low complexity.