Resource management in particle filtering for multiple object tracking

Pan Pan; Dan Schonfeld

doi:10.1117/12.766567

28 January 2008 Resource management in particle filtering for multiple object tracking

Pan Pan, Dan Schonfeld

Author Affiliations +

Proceedings Volume 6822, Visual Communications and Image Processing 2008; 68220E (2008) https://doi.org/10.1117/12.766567
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

This paper presents a novel particle allocation approach to particle filtering for multiple object tracking which minimizes the total tracking distortion given a fixed number of particles over a video sequence. Under the framework of distributed multiple object tracking, we propose the dynamic proposal variance and optimal particle number allocation algorithm for multi-object tracking to allocate particles among multiple targets as well as multiple frames. Experimental results show the superior performance of our proposed algorithm to traditional particle allocation methods, i.e. a fixed number of particles for each object in each frame. To the best of our knowledge, our approach is the first to provide an optimal allocation of a fixed number of particles among multiple objects and multiple frames.

Citation Download Citation

Pan Pan and Dan Schonfeld "Resource management in particle filtering for multiple object tracking", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68220E (28 January 2008); https://doi.org/10.1117/12.766567

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE