Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones

Hazal Lezki, Berker Logoglu, Akif Akpinar, Mehmet Kerim Yucel, Ahu Ozturk, Aykut Erdem, Erkut Erdem

Abstract: Moving object detection is an imperative task in computer vision, where it is primarily used for surveillance applications. With the increasing availability of low-altitude aerial vehicles, new challenges for moving object detection have surfaced, both for academia and industry. In this paper, we propose a new approach that can detect moving objects efficiently and handle parallax cases. By introducing sparse flow based parallax handling and downscale processing, we push the boundaries of real-time performance with 16 FPS on limited embedded resources (a five-fold improvement over existing baselines), while managing to perform comparably or even improve the state-of-the-art in two different datasets. We also present a roadmap for extending our approach to exploit multi-modal data in order to mitigate the need for parameter tuning.