Object Tracking in 3-D Space with Passive Acoustic Sensors using Particle Filter

2011 ◽  
Vol 5 (9) ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Acoustic Sensors ◽  
Passive Acoustic

Chaotic particle filter for visual object tracking

2018 ◽  
Vol 53 ◽  
pp. 1-12 ◽  
Author(s):  
Marjan Firouznia ◽  
Karim Faez ◽  
Hamidreza Amindavar ◽  
Javad Alikhani Koupaei
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking

scholarly journals Particle Filter with Gaussian Weighting for Vehicle Tracking

2011 ◽  
Vol 15 (6) ◽  
pp. 681-686
Author(s):  
Indah Agustien Siradjuddin ◽  
◽  
Muhammad Rahmat Widyanto ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Gaussian Distribution ◽  
Likelihood Function ◽  
Target Object ◽  
Video Data ◽  
Vehicle Tracking ◽  
Computational Time ◽  
Filter Method ◽  
Vehicle Motion

To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.

scholarly journals Real Time Color Object Tracking on Cell Broadband Engine Using Particle Filters

2010 ◽  
Vol 14 (3) ◽  
pp. 272-280 ◽  
Author(s):  
Norikazu Ikoma ◽  
◽  
Akihiro Asahara ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Real Time ◽  
Visual Tracking ◽  
Single Object ◽  
Cell Broadband Engine ◽  
Time Performance ◽  
Large Size ◽  
Particle Filter Algorithm ◽  
Local Store

Real time visual tracking by particle filter has been implemented on Cell Broadband Engine in parallel. Major problem for the implementation is small size of Local Store (LS) in SPEs (Synergistic PEs), which are computational cores, to deal with image of large size. As a first step for the implementation, we focus on color single object tracking, which is one of the most simple case of visual tracking. By elaborating to compress the color extracted image into bit-wise representation of binary image, all information of the color extracted image can be stored in LS for 640×480 size of original image. By applying our previous implementation of general particle filter algorithm on Cell/B.E. to this specific case, we have achieved real time performance of visual tracking on PlayStation®3 about 7 fps with a camera of maximum 15 fps.

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