Efficient data association for move-stop-move target tracking

2008 ◽  
Author(s):  
T. Sathyan ◽  
Mike McDonald ◽  
T. Kirubarajan
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Efficient Data

Efficient data association to targets for tracking in passive wireless sensor networks

2018 ◽  
Vol 75-76 ◽  
pp. 19-32 ◽  
Author(s):  
Changhyuk An ◽  
Youngwon Kim An ◽  
Seong-Moo Yoo ◽  
B. Earl Wells
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Association ◽  
Wireless Sensor ◽  
Efficient Data

scholarly journals A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2180 ◽  
Author(s):  
Xiao Chen ◽  
Yaan Li ◽  
Yuxing Li ◽  
Jing Yu ◽  
Xiaohua Li
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Cluttered Environment

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

2018 ◽  
Vol 10 (9) ◽  
pp. 1347 ◽  
Author(s):  
Ting Chen ◽  
Andrea Pennisi ◽  
Zhi Li ◽  
Yanning Zhang ◽  
Hichem Sahli
Keyword(s):  
Object Tracking ◽  
Target Tracking ◽  
Moving Objects ◽  
State Of The Art ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Challenging Problem ◽  
Compressive Tracking ◽  
Vehicle Motion ◽  
Airborne Vehicle

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.

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