Name
Abstract | ||
|---|---|---|
Aircraft tracking plays a key and important role in the Sense-and-Avoid system of Unmanned Aerial Vehicles (UAVs). This paper presents a novel robust visual tracking algorithm for UAVs in the midair to track an arbitrary aircraft at real-time frame rates, together with a unique evaluation system. This visual algorithm mainly consists of adaptive discriminative visual tracking method, Multiple-Instance (MI) learning approach, Multiple-Classifier (MC) voting mechanism and Multiple-Resolution (MR) representation strategy, that is called Adaptive M3 tracker, i.e. AM3. In this tracker, the importance of test sample has been integrated to improve the tracking stability, accuracy and real-time performances. The experimental results show that this algorithm is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant surrounding illumination, partial aircraft occlusion, blur motion, rapid pose variation and onboard mechanical vibration, low computation capacity and delayed information communication between UAVs and Ground Station (GS). To our best knowledge, this is the first work to present this tracker for solving online learning and tracking freewill aircraft/intruder in the UAVs. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICRA.2014.6907659 | ICRA |
Keywords | Field | DocType |
multiple-classifier voting mechanism,adaptive discriminative visual tracking method,ground station,sense-and-avoid system,multiple-instance learning approach,blur motion,learning (artificial intelligence),visual algorithm,robust real-time vision-based aircraft tracking,mobile robots,unmanned aerial vehicles,control engineering computing,multiple-resolution representation strategy,online learning,mr representation strategy,tracking stability,pose estimation,delayed information communication,autonomous aerial vehicles,object tracking,freewill aircraft/intruder tracking,partial aircraft occlusion,computation capacity,onboard mechanical vibration,mc voting mechanism,uav,stability,adaptive m3 tracker,aircraft control,illumination,robust visual tracking algorithm,mi learning approach,robot vision,pose variation,image motion analysis,learning artificial intelligence | BitTorrent tracker,Computer vision,Tracking system,Pose,Control engineering,Eye tracking,Video tracking,Artificial intelligence,Frame rate,Engineering,Discriminative model,Mobile robot | Conference |
ISSN | Citations | PageRank |
1050-4729 | 7 | 0.49 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changhong Fu | 1 | 90 | 20.62 |
| Adrian Carrio | 2 | 56 | 6.72 |
| Miguel A. Olivares-Mendez | 3 | 89 | 8.97 |
| Ramon Suarez-Fernandez | 4 | 21 | 1.73 |
| Pascual Campoy | 5 | 436 | 46.75 |