Visual tracking using super-pixel local weighted measure and inverse sparse model

doi:10.1109/CompComm.2017.8322891

	Visual tracking using super-pixel local weighted measure and inverse sparse model
	Liu, Weirong1 ; Wu, Hailong 1; Zhao, Junqi 1; Liu, Jie 2; Liu, Chaorong3
	2017-07-02
会议名称	3rd IEEE International Conference on Computer and Communications, ICCC 2017
会议录名称	2017 3rd IEEE International Conference on Computer and Communications, ICCC 2017
卷号	2018-January
页码	2020-2024
会议日期	December 13, 2017 - December 16, 2017
会议地点	Chengdu, China
出版者	Institute of Electrical and Electronics Engineers Inc.
摘要	Recently, sparse representation and super-pixel mid-level cues have been applied to visual tracking with demonstrated success. The authors propose an efficient and precise tracking algorithm, it has three features: super-pixel local weight, a novel sparse model and double threshold scheme to determine weight update. In the super-pixel local weight, the authors segment the surrounding target regions into super-pixels in stage of training, and then apply mean-shift on the surrounding target regions super-pixels to obtain clusters. A confidence map is calculated according to the clusters. After that, we combine the template super-pixels with the confidence map to compute the initial super-pixel local weight. The inverse sparse model is adopted to improve the tracking efficiency. For double threshold super-pixel updates, we compare the super-pixels update areas with the double threshold to decision update or not. Experimental results show that our method is robust to illumination change, human body posture change and heavy occlusion. © 2017 IEEE.
关键词	Inverse problems Confidence maps Double threshold Human body postures Illumination changes Local Weight Precise tracking Sparse representation Visual Tracking
DOI	10.1109/CompComm.2017.8322891
收录类别	EI
语种	英语
EI入藏号	20182905553011
EI主题词	Pixels
来源库	Compendex
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/117991
专题	党委教师工作部（人事处、教师发展中心）电气工程与信息工程学院
作者单位	1.College of Electrical and Information Engineering, Lanzhou University of Technology, China; 2.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, China; 3.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou, China
第一作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Liu, Weirong,Wu, Hailong,Zhao, Junqi,et al. Visual tracking using super-pixel local weighted measure and inverse sparse model[C]:Institute of Electrical and Electronics Engineers Inc.,2017:2020-2024.