计算机应用研究2017，Vol.34Issue(12)：3828-3833,6.DOI:10.3969/j.issn.1001-3695.2017.12.070
基于双模型融合的自适应目标跟踪算法
Adaptive target tracking algorithm based on fusion of two models
摘要
Abstract
To deal with illumination variation,background clutters and object deformation,this paper proposed an adaptive tracking algorithm using the fusion of background suppressed HS histogram model and correlation filter model.Firstly,it introduced a non-linear kernelized correlation filter tracking model.Secondly,it proposed a background suppressed HS histogram tracking model.This model separated luminance component to reduce illumination interference and used a background weighted method to highlight object information.Furthermore,this paper proposed an adaptive fusion strategy.According to the HS similarity between object and each background patch,it adjusted the fusion weight of two models dynamically to reduce the influence of background clusters and pose variation.Finally,it used a scale pyramid estimation strategy to handle scale variations.Experimental results on public datasets demonstrate that,compared with other trackers,this algorithm performs better on complex factors,such as illumination variation and background clusters,and meets engineering application real-time requirements.关键词
目标跟踪/相关滤波/HS直方图/尺度金字塔/自适应融合Key words
object tracking/correlation filter/HS histogram/scale pyramid/adaptive fusion分类
信息技术与安全科学引用本文复制引用
王艳川,黄海,李邵梅,王亚文..基于双模型融合的自适应目标跟踪算法[J].计算机应用研究,2017,34(12):3828-3833,6.基金项目
国家自然科学基金资助项目(61379151) （61379151）
创新群体资助项目(61521003) （61521003）
国家科技支撑计划资助项目(2014BAH30B01) （2014BAH30B01）
青年科学基金资助项目(61601513) （61601513）
