Paper
14 February 2020 An improved detection and tracking method for small dim moving target based on particle filter
Author Affiliations +
Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 114290U (2020) https://doi.org/10.1117/12.2539336
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
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.
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Nan Zhang, Feng Li, Xiaotian Lu, Xue Yang, and Lei Xin "An improved detection and tracking method for small dim moving target based on particle filter", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290U (14 February 2020); https://doi.org/10.1117/12.2539336
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KEYWORDS
Particle filters

Target detection

Detection and tracking algorithms

Signal to noise ratio

Electronic filtering

Image processing

Surveillance systems

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