Image Moment-based Object Tracking and Shape Estimation for Complex Motions

2018 ◽  
Author(s):  
Gang Yao ◽  
Kyle Hunte ◽  
Ashwin Dani
Keyword(s):  
Object Tracking ◽  
Shape Estimation ◽  
Image Moment

scholarly journals Image Moment-Based Extended Object Tracking for Complex Motions

2020 ◽  
Vol 20 (12) ◽  
pp. 6560-6572 ◽  
Author(s):  
Gang Yao ◽  
Ryan Saltus ◽  
Ashwin Dani
Keyword(s):  
Object Tracking ◽  
Extended Object ◽  
Image Moment

Multi-radar data fusion for object tracking and shape estimation

1996 ◽  
Vol 48 (3) ◽  
pp. 235-239 ◽  
Author(s):  
L. Favalli ◽  
P. Gamba ◽  
T. Gatti ◽  
A. Mecocci
Keyword(s):  
Data Fusion ◽  
Object Tracking ◽  
Radar Data ◽  
Shape Estimation

scholarly journals Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

2020 ◽  
Vol 21 (7) ◽  
pp. 2874-2893 ◽  
Author(s):  
Sascha Steyer ◽  
Christian Lenk ◽  
Dominik Kellner ◽  
Georg Tanzmeister ◽  
Dirk Wollherr
Keyword(s):  
Object Tracking ◽  
Nonlinear Dynamic ◽  
Dynamic State ◽  
Shape Estimation ◽  
Grid Based

Multiple-Object Tracking

2011 ◽  
Vol 58 (3) ◽  
pp. 196-200 ◽  
Author(s):  
K. Botterill ◽  
R. Allen ◽  
P. McGeorge
Keyword(s):  
Object Tracking ◽  
Cognitive Processes ◽  
Spatial Location ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

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