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Proceedings Paper

The improved adaptive mean-shift algorithm of single target tracking for infrared images
Author(s): Jing Han II; Qin Wang; Yi Zhang III; Lian-Fa Bai IV; Jiang Yue IV
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Paper Abstract

In the infrared scene, locally adaptive regression kernels (LARK) feature has the advantage of sensitive to the change of small structure. The probability distribution of the target in the continuously adaptive mean -shift (CamShift) algorithm of single target tracking is weighted by the similarity of feature global matching . It can weaken the interference of background. In order to robustly track infrared target with shape changes, global matching is turned into local statistical matching according to the invariance of target local structure. The number of similar characteristics in the area around a point is used as the weights.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061F (9 August 2018); doi: 10.1117/12.2503216
Show Author Affiliations
Jing Han II, Nanjing Univ. of Science and Technology (China)
Qin Wang, Nanjing Univ. of Science and Technology (China)
Yi Zhang III, Nanjing Univ. of Science and Technology (China)
Lian-Fa Bai IV, Nanjing Univ. of Science and Technology (China)
Jiang Yue IV, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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