Moving Object Detection Based on a New Level Set Algorithm Using Directional Speed Function

Abstract

In this paper, a moving object detection method is proposed based on a level set algorithm of which speed function employs three properties based on human visual characteristics. The speed function is composed of three factors: directional filtered difference, proximity weighted spatial edgeness, and directional intensity consistency. For the directional filtered difference factor, directional filtering of the difference image between background and current images is introduced to utilize temporal edgeness along a detected contour. The edgeness in the current image is also employed for an initial estimation of moving object regions. The last factor, directional intensity consistency, is based on the continuity assumption of gray-level intensities along an estimated contour. The effectiveness of the proposed algorithm is shown with four real image sequences in terms of objective detection accuracies for various experimental conditions.