Particle Filter Tuning Using Continuous Opinion Dynamic Optimizer for Object Tracking

  Shallu Rani,  Sukhjinder Kaur

Object tracking is a technique that is being used from past centuries. This technique includes an application called surveillance that helps to detect the threats on public places that may be the crowded public place or a mall or public transport with the help of CCTV cameras. In the surveillance system, several vision based methods are utilized for various objects. Some of the methods which are used for tracking includes background modeling, particle filter etc. The proposed method combines the background modeling and particle filter to track multiple objects. In the object tracking process the object is considered to be in motion if its location is changing with respect to its background. By using background subtraction method the change of frame is detected, in case if there is no difference between the two frames then the object is considered to be in a static position. The proposed work is to use human opinion dynamic algorithm for particle filter tuning in order to make the prediction more effective. As the proposed algorithm involved solving the minimization problem for each drawn sample with the proposed model, the proposed system has been planned for real-time applications. The results will be presented to verify the same.

Keywords- Tracking, prediction, computer vision, background subtraction etc.

Unique Identification Number - IJEDR1604132
Page Number(s) - 877-882
Pubished in - Volume 4 | Issue 4 | December 2016
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Shallu Rani,  Sukhjinder Kaur,   "Particle Filter Tuning Using Continuous Opinion Dynamic Optimizer for Object Tracking", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 4, pp.877-882, December 2016, Available at :http://www.ijedr.org/papers/IJEDR1604132.pdf