scholarly journals Multiple Object Tracking with Kernelized Correlation Filters in Urban Mixed Traffic

2017 ◽  
Author(s):  
Yuebin Yang ◽  
Guillaume-Alexandre Bilodeau
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Mixed Traffic ◽  
Correlation Filters ◽  
Multiple Object ◽  
Kernelized Correlation Filters ◽  
Urban Mixed Traffic

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

scholarly journals Dense Scene Multiple Object Tracking with Box-Plane Matching

2020 ◽  
Author(s):  
Jinlong Peng ◽  
Yueyang Gu ◽  
Yabiao Wang ◽  
Chengjie Wang ◽  
Jilin Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object
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