scholarly journals Object Tracking from Unstabilized Platforms by Particle Filtering with Embedded Camera Ego Motion

2009 ◽  
Author(s):  
Carlos R. del-Blanco ◽  
Narciso Garcia ◽  
Luis Salgado ◽  
Fernando Jaureguizar
Keyword(s):  
Object Tracking ◽  
Particle Filtering

Robust object tracking via integration of particle filtering with deep detection

2019 ◽  
Vol 87 ◽  
pp. 112-124 ◽  
Author(s):  
Filiz Gurkan ◽  
Bilge Gunsel ◽  
Caner Ozer
Keyword(s):  
Object Tracking ◽  
Particle Filtering

Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework

2011 ◽  
Vol 341-342 ◽  
pp. 790-797 ◽  
Author(s):  
Zhi Yan Xiang ◽  
Tie Yong Cao ◽  
Peng Zhang ◽  
Tao Zhu ◽  
Jing Feng Pan
Keyword(s):  
Principal Component Analysis ◽  
Object Tracking ◽  
Particle Filtering ◽  
Principal Component ◽  
Component Analysis ◽  
Dimensional Subspace ◽  
Target Object ◽  
Scale Invariant ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

In this paper, an object tracking approach is introduced for color video sequences. The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework. Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently. Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object. At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking. Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.

Particle filtering with multiple cues for object tracking in video sequences

2005 ◽  
Author(s):  
Paul A. Brasnett ◽  
Lyudmila Mihaylova ◽  
Nishan Canagarajah ◽  
David Bull
Keyword(s):  
Object Tracking ◽  
Particle Filtering ◽  
Video Sequences ◽  
Multiple Cues
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