The improved adaptive mean-shift algorithm of single target tracking for infrared images

Jing Han II; Qin Wang; Yi Zhang III; Lian-Fa Bai IV; Jiang Yue IV

doi:10.1117/12.2503216

9 August 2018 The improved adaptive mean-shift algorithm of single target tracking for infrared images

Jing Han II, Qin Wang, Yi Zhang III, Lian-Fa Bai IV, Jiang Yue IV

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061F (2018) https://doi.org/10.1117/12.2503216
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Jing Han II, Qin Wang, Yi Zhang III, Lian-Fa Bai IV, and Jiang Yue IV "The improved adaptive mean-shift algorithm of single target tracking for infrared images", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061F (9 August 2018); https://doi.org/10.1117/12.2503216

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