scholarly journals MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 31934-31945 ◽  
Author(s):  
Zheng Tang ◽  
Jenq-Neng Hwang
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Appearance Model ◽  
Multiple Object

Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model

2012 ◽  
Vol 34 (12) ◽  
pp. 2420-2440 ◽  
Author(s):  
Weiming Hu ◽  
Xi Li ◽  
Wenhan Luo ◽  
Xiaoqin Zhang ◽  
S. Maybank ◽  
...  
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Appearance Model ◽  
Multiple Object

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.

Location and identity are entangled in multiple-object tracking

2010 ◽  
Author(s):  
Todd S. Horowitz ◽  
Michael A. Cohen ◽  
Yair Pinto ◽  
Piers D. L. Howe
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object
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