Title
FPGA-based Real-Time Object Tracking Using a Particle Filter with Stream Architecture
Abstract
This paper deals with the real-time FPGA implementation of the posterior system state estimation in dynamic state-space models using a particle filter. The system is constructed by parallel resampling (FO-resampling) algorithm on a stream-based architecture. In particular, the system consists of three steps: prediction, likelihood calculation and resampling. Since the resampling is accomplished in a synchronized area, our approach enhances the object tracking system especially efficiency and performance. The result shows that the amount of FPGA resource utilizes for the simulation of red-color soccer ball tracking compared with the available usage. Moreover, we evaluate the tracker detection rate and the accuracy of object tracking with the calculation of average and maximum tracking errors.
Year
DOI
Venue
2016
10.1109/CANDAR.2016.0079
2016 Fourth International Symposium on Computing and Networking (CANDAR)
Keywords
Field
DocType
particle filter,FPGA,parallel resampling,stream processing
Computer vision,Histogram,Architecture,Computer science,Particle filter,Field-programmable gate array,Real-time computing,Ball tracking,Video tracking,Artificial intelligence,Resampling
Conference
ISSN
ISBN
Citations 
2379-1888
978-1-5090-2656-2
0
PageRank 
References 
Authors
0.34
6
5
Name
Order
Citations
PageRank
Akane Tahara101.01
Yoshiki Hayashida201.01
Theint Theint Thu301.01
Yuichiro Shibata415737.99
Kiyoshi Oguri514633.63