Real-Time Obstacle Detection and Tracking for Sense-and-Avoid Mechanism in UAVs

Abstract

Obstacle detection and tracking is an important research topic in computer vision with a number of practical applications. Though an ample amount of research has been done in this domain, implementing automatic obstacle detection and tracking in real time is still a big challenge. To address this issue, we propose a fast and robust obstacle detection and tracking approach by integrating an adaptive obstacle detection strategy within a kernelized correlation filter (KCF) framework in this paper. A suitable salient object detection method autoinitializes the KCF tracker for this purpose. Moreover, an adaptive obstacle detection strategy is proposed to refine the location and boundary of the object when the confidence value of the tracker drops below a predefined threshold. In addition, a reliable postprocessing technique is implemented to accurately localize the obstacle from a saliency map recovered from the search region. The proposed approach has been extensively tested through quantitative and qualitative evaluations on a number of challenging datasets. The experiments demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in terms of tracking speed and accuracy.