电子学报2018，Vol.46Issue(2)：440-446,7.DOI:10.3969/j.issn.0372-2112.2018.02.025
基于多表观特征子模型更新的鲁棒视觉跟踪
Robust Visual Tracking Based on Sub-model Updating of Multiple Apparent Features
摘要
Abstract
In computer vision tracking,the traditional model updating has poor robustness in solving the problem of occlusion,illumination change and self rotation.To improve these problems,this study proposes a new visual object tracking method.The algorithm firstly builds a candidate update sub-model library.Secondly,it determines the position and informa-tion of the current target by fusing the three complementary features of the tracking based on Particle Filter.Finally,the algo-rithm divides the three characteristic histogram of the target and the candidate model library to calculate the similarity of the reliability weights,then determines whether the candidate sub-model library and current sub-model can be updated.Results show that the algorithm can effectively select to update the sub-model.Rather than the contrast algorithms,our method can a-chieve a better tracking accuracy to deal with the situation of occlusion,illumination change and self rotation.The proposed method updates the target model effectively and keeps the good robustness under various tracking scenarios.关键词
视觉跟踪/粒子滤波/模型更新/多特征融合/候选子模型库/加权相似度Key words
visual tracking/particle filter/model update/multi feature fusion/candidate sub-model library/weighted similarity分类
信息技术与安全科学引用本文复制引用
范舜奕,管桦,侯志强,余旺盛,戴铂..基于多表观特征子模型更新的鲁棒视觉跟踪[J].电子学报,2018,46(2):440-446,7.基金项目
国家自然科学基金(No.61473309,No.61703423) （No.61473309,No.61703423）
陕西省自然科学基金(No.2015JM6269,No.2016JM6050) （No.2015JM6269,No.2016JM6050）
