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Remote Sens. 2018, 10(9), 1347; https://doi.org/10.3390/rs10091347

A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

1
Department Electronics and Informatics, AVSP Lab, Vrije Universiteit Brussels, 1050 Brussels, Belgium
2
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
3
Interuniversity Microelectronics Center, 3001 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Received: 13 July 2018 / Revised: 14 August 2018 / Accepted: 19 August 2018 / Published: 23 August 2018
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Abstract

Multi-Object Tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, changes of size, appearance and motion of the moving objects and occlusions caused by the interaction between moving and static objects in the scene. To deal with these problems, this work proposes a four-stage hierarchical association framework for multiple object tracking in airborne video. The proposed framework combines Data Association-based Tracking (DAT) methods and target tracking using a compressive tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne videos show significant tracking improvement compared to existing state-of-the-art methods. View Full-Text
Keywords: multiple object tracking; airborne video; tracklet confidence; hierarchical association framework multiple object tracking; airborne video; tracklet confidence; hierarchical association framework
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Chen, T.; Pennisi, A.; Li, Z.; Zhang, Y.; Sahli, H. A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos. Remote Sens. 2018, 10, 1347.

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