Multi target tracking using multiple independent particle filters for video surveillance

2011 ◽  
Author(s):  
YoungJoon Chai ◽  
JinYong Park ◽  
KwangJin Yoon ◽  
TaeYong Kim
Keyword(s):  
Target Tracking ◽  
Video Surveillance ◽  
Particle Filters ◽  
Independent Particle ◽  
Multi Target Tracking

scholarly journals Underwater Multi-Target Tracking with Particle Filters

2018 ◽  
Author(s):  
I. Masmitja ◽  
S. Gomariz ◽  
J. Del Rio ◽  
P. J. Bouvet ◽  
J. Aguzzi
Keyword(s):  
Target Tracking ◽  
Particle Filters ◽  
Multi Target Tracking

scholarly journals Color and motion-based particle filter target tracking in a network of overlapping cameras with multi-threading and GPGPU

2013 ◽  
Vol 23 (1) ◽  
pp. 9-16
Author(s):  
Jorge Francisco Madrigal Díaz ◽  
Jean-Bernard Hayet
Keyword(s):  
Target Tracking ◽  
Particle Filters ◽  
Target Position ◽  
General Purpose ◽  
Video Sequences ◽  
Central Processing ◽  
Graphical Processing Units ◽  
Independent Particle ◽  
Likelihood Model ◽  
Graphical Processing

This paper describes an efficient implementation of multiple-target multiple-view tracking in video-surveillance sequences. It takes advantage of the capabilities of multiple core Central Processing Units (CPUs) and of graphical processing units under the Compute Unifie Device Arquitecture (CUDA) framework. The principle of our algorithm is 1) in each video sequence, to perform tracking on all persons to track by independent particle filters and 2) to fuse the tracking results of all sequences. Particle filters belong to the category of recursive Bayesian filters. They update a Monte-Carlo representation of the posterior distribution over the target position and velocity. For this purpose, they combine a probabilistic motion model, i.e. prior knowledge about how targets move (e.g. constant velocity) and a likelihood model associated to the observations on targets. At this first level of single video sequences, the multi-threading library Threading Buildings Blocks (TBB) has been used to parallelize the processing of the per-target independent particle filters. Afterwards at the higher level, we rely on General Purpose Programming on Graphical Processing Units (generally termed as GPGPU) through CUDA in order to fuse target-tracking data collected on multiple video sequences, by solving the data association problem. Tracking results are presented on various challenging tracking datasets.

Improved particle filters for multi-target tracking

2012 ◽  
Vol 231 (2) ◽  
pp. 602-611 ◽  
Author(s):  
Vasileios Maroulas ◽  
Panos Stinis
Keyword(s):  
Target Tracking ◽  
Particle Filters ◽  
Multi Target Tracking

Multi-target tracking with an event-based vision sensor and a partial-update GMPHD filter

2019 ◽  
Vol 2019 (13) ◽  
pp. 127-1-127-7
Author(s):  
Benjamin J. Foster ◽  
Dong Hye Ye ◽  
Charles A. Bouman
Keyword(s):  
Target Tracking ◽  
Vision Sensor ◽  
Multi Target Tracking ◽  
Event Based

Shared-memory parallel multi-target tracking

2009 ◽  
Vol 28 (9) ◽  
pp. 2303-2305
Author(s):  
Xiao-gang WANG ◽  
Xiao-juan WU ◽  
Xin ZHOU ◽  
Xiao-yan ZHANG
Keyword(s):  
Target Tracking ◽  
Shared Memory ◽  
Multi Target Tracking
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