Share Email Print
cover

Proceedings Paper

An improved detection and tracking method for small dim moving target based on particle filter
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Particle filtering is a key technique for moving targets detection and tracking in the field of remote surveillance system and air defense systems. Moving targets can be tracked by particle filter without registration. However, standard particle filtering cannot suite for high-precision tracking and track small dim moving targets occupying a few pixels in image, having low signal-to-noise ratio (SNR) and always flicking. To solve this problem, an improved algorithm is proposed to achieve detection and tracking for small dim moving targets. In the new algorithm, the prediction process of particle filter is improved by a linear regression method. It is applicable to the sequential images where the moving targets become smaller and dimmer gradually. Small dim targets can be detected and tracked directly with low SNR and without registration. The trajectory of the moving target is learned automatically through the past state of the moving target, and the trajectory is used for generating the importance density function. The importance density function is used as the prior probability in particle filter to sample and update particles. Through continuously learning and updating the trajectory of the moving target, the tracking accuracy is improved. Experimental results show that the tracking accuracy of the moving targets is greatly improved, and small dim moving targets can be detected and tracked without registration.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290U (14 February 2020); doi: 10.1117/12.2539336
Show Author Affiliations
Nan Zhang, Qian Xuesen Lab. of Space Technology (China)
Feng Li, Qian Xuesen Lab. of Space Technology (China)
Xiaotian Lu, Qian Xuesen Lab. of Space Technology (China)
Xue Yang, Qian Xuesen Lab. of Space Technology (China)
Lei Xin, Qian Xuesen Lab. of Space Technology (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray