Constrained optical flow for aerial image change detection

Nowadays, the amount of video data acquired for observation or surveillance applications is overwhelming. Due to these huge volumes of video data, focusing the attention of operators on "areas of interest" requires change detection algorithms. In the particular task of aerial observation, camera motion and viewpoint differences introduce parallax effects, which may substantially affect the reliability and the efficiency of automatic change detection. In this paper, we introduce a novel approach for change detection that considers the geometric aspects of camera sensors as well as the statistical properties of changes. Indeed, our method is based on optical flow matching, constrained by the epipolar geometry, and combined with a statistical change decision criterion. The good performance of our method is demonstrated through our new public Aerial Imagery Change Detection (AICD) dataset of labeled aerial images.

1. INTRODUCTION

These last few years have witnessed a rapid development of video acquisition devices, which are becoming cheaper and more efficient. In the context of observation via airborne cameras, the volume of data being acquired is overwhelming human operators and most of it is stored unprocessed or lost. As a consequence, it becomes necessary to develop solutions addressing this bottleneck.