LI Lan,ZHANG Yun,MA Shaobin.Face tracking based on improved fusion of gradient and color mean shift particle filter[J].Journal of Yanbian University,2018,44(02):139-142.
改进的梯度与肤色融合均值移动粒子滤波人脸跟踪
- Title:
- Face tracking based on improved fusion of gradient and color mean shift particle filter
- Keywords:
- mean shift; particle filter; color feature; gradient feature; features fusion; face tracking
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
- 针对单一肤色特征的跟踪算法鲁棒性不高的问题,提出一种多特征融合的均值移动粒子滤波(MSPF)跟踪算法.该算法用肤色特征与梯度特征表示候选目标,通过粒子滤波与均值移动算法进行特征融合.实验结果表明,该算法能够较好地提高跟踪效率,并对光照、人脸遮挡和人脸旋转等有一定的适应性.
- Abstract:
- Object tracking by using single color feature results in a problem of low robustness. Mean shift particle filter(MSPF)algorithm based on multi-features fusion is proposed in this paper. The proposed method uses the color and gradient features to represent the candidate target, utilizes feature fusion in mean-shift and particle filter algorithms, and efficiently reduces the problem of particle degradation and tracking instability. Experimental results indicate the algorithm can improve the tracking accuracy, and have good adaptability to the illumination, face occlusion and rotation.
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相似文献/References:
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备注/Memo
收稿日期: 2017-12-12
作者简介: 李岚(1978—),女,副教授,研究方向为智能信息处理、数字媒体技术.
基金项目: 兰州文理学院科研项目(2013YBTS02); 甘肃省高等学校科研一般项目(2015B-133; 2016A-105)