DocumentCode
3002460
Title
Object Tracking Based on Particle Filter and Scale Invariant Feature Transform
Author
Jiang, Min ; Zhang, Lei ; Huang, Yanli
Author_Institution
Coll. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China
fYear
2010
fDate
29-31 Oct. 2010
Firstpage
1
Lastpage
4
Abstract
Particle filter is a popular stochastic tracker for object tracking. In this paper, we proposed a novel algorithm for object tracking based on particle filter and Scale Invariant Feature Transform (SIFT). The result of SIFT matching does not adopt to reweight the particles as previous methods, we adopts a hybrid schema to supplement the particle distribution of traditional factor sampling with importance sampling. Experiments show that the proposed algorithm yields a more robust tracking result.
Keywords
image matching; importance sampling; object detection; particle filtering (numerical methods); target tracking; transforms; SIFT matching; factor sampling; importance sampling; object tracking; particle distribution; particle filter; scale invariant feature transform; stochastic tracker; Atmospheric measurements; Feature extraction; Image color analysis; Monte Carlo methods; Particle filters; Particle measurements; Tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Multimedia Technology (ICMT), 2010 International Conference on
Conference_Location
Ningbo
Print_ISBN
978-1-4244-7871-2
Type
conf
DOI
10.1109/ICMULT.2010.5631001
Filename
5631001
Link To Document