Efficient Road Detection and Tracking for Unmanned Aerial Vehicle

• Published in: IEEE transactions on intelligent transportation systems Vol. 16; no. 1; pp. 297 - 309
• Main Authors: Hailing Zhou, Hui Kong, Lei Wei, Creighton, Douglas, Nahavandi, Saeid
• Format: Journal Article
• Language: English
• Published: IEEE 01.02.2015
• Subjects: Cameras; Eigenvalues and eigenfunctions; GraphCut algorithm; homography; Image color analysis; Image segmentation; Real-time systems; road detection; road tracking; Roads; unmanned aerial vehicle (UAV); Videos; GraphCut algorithm; road tracking; unmanned aerial vehicle (UAV); homography; road detection
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2014.2331353

An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground-vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 × 595 videos on average.