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Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation
March 2010 (vol. 32 no. 3)
pp. 517-529
ASCII Text
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Walter Nunziati, Stan Sclaroff, Alberto Del Bimbo, "Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 517-529, March, 2010.
 
 
BibTex
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@article{ 10.1109/TPAMI.2009.35,
author = {Walter Nunziati and Stan Sclaroff and Alberto Del Bimbo},
title = {Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {3},
issn = {0162-8828},
year = {2010},
pages = {517-529},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.35},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation
IS - 3
SN - 0162-8828
SP517
EP529
EPD - 517-529
A1 - Walter Nunziati,
A1 - Stan Sclaroff,
A1 - Alberto Del Bimbo,
PY - 2010
KW - Registration
KW - invariants
KW - similarity measures
KW - cross ratio.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Walter Nunziati, Media Integration and Communication Center, Firenze
Stan Sclaroff, Boston University, Boston
Alberto Del Bimbo, Università degli Studi di Firenze, Firenze
Identifying correspondences between trajectory segments observed from nonsynchronized cameras is important for reconstruction of the complete trajectory of moving targets in a large scene. Such a reconstruction can be obtained from motion data by comparing the trajectory segments and estimating both the spatial and temporal alignments. Exhaustive testing of all possible correspondences of trajectories over a temporal window is only viable in the cases with a limited number of moving targets and large view overlaps. Therefore, alternative solutions are required for situations with several trajectories that are only partially visible in each view. In this paper, we propose a new method that is based on view-invariant representation of trajectories, which is used to produce a sparse set of salient points for trajectory segments observed in each view. Only the neighborhoods at these salient points in the view--invariant representation are then used to estimate the spatial and temporal alignment of trajectory pairs in different views. It is demonstrated that, for planar scenes, the method is able to recover with good precision and efficiency both spatial and temporal alignments, even given relatively small overlap between views and arbitrary (unknown) temporal shifts of the cameras. The method also provides the same capabilities in the case of trajectories that are only locally planar, but exhibit some nonplanarity at a global level.

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Index Terms:
Registration, invariants, similarity measures, cross ratio.
Citation:
Walter Nunziati, Stan Sclaroff, Alberto Del Bimbo, "Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 517-529, March 2010, doi:10.1109/TPAMI.2009.35
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