scholarly journals Enhancing the association in multi‐object tracking via neighbor graph

2021 ◽  
Author(s):  
Tianyi Liang ◽  
Long Lan ◽  
Xiang Zhang ◽  
Xindong Peng ◽  
Zhigang Luo
Keyword(s):  
Object Tracking ◽  
Neighbor Graph

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.

Predictable paths increase the use of motion in object tracking

2010 ◽  
Author(s):  
Adriane E. Seiffert ◽  
Rebecca St. Clair
Keyword(s):  
Object Tracking

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

Object Tracking with RFID

2011 ◽  
Vol 131 (4) ◽  
pp. 441-447
Author(s):  
Kou Yamada ◽  
Seiichi Uchida ◽  
Rin-ichiro Taniguchi
Keyword(s):  
Object Tracking

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

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