Adaptive Object Detection and Visibility Improvement in Foggy Image

Abstract

Detecting objects of interest and obtaining their clear visual appearances are critical requirements for visual surveillance systems. In this paper we propose a novel algorithm to detect foreground objects from video sequences with fog and then enhance their visibilities. First, we propose a novel metric to measure the image fog property to decide whether the image scene is obscured by fog or not. Second, if there is heavy fog in the scene, a novel approach for object detection based on an atmospheric scattering model is proposed. This novel approach can be used to detect not only newly entering objects but also sojourned objects. Once the foreground object is detected, we enhance its visibility only to avoid processing the whole image. Our proposed algorithm is tested with some surveillance video under different fog conditions. Experimental results show that the proposed approach is efficient and efficient for foreground object detection and visibility enhancement under fog weather conditions.