Multi-technique object tracking approach- A reinforcement paradigm

被引:3
|
作者
Samuel, Oluwarotimi Williams [1 ,2 ]
Asogbon, Grace Mojisola [1 ]
Sangaiah, Arun Kumar [3 ]
Li, Guanglin [1 ,2 ]
机构
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, CAS Key Lab Human Machine Intelligence Synergy Sy, Shenzhen 518055, Peoples R China
[2] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen 518055, Peoples R China
[3] VIT Univ, Sch Comp Sci & Engn, Vellore 632074, Tamil Nadu, India
关键词
Object tracking; Surveillance; CamShift; Unscented particle filter; Corrected background weighted histogram; FILTER;
D O I
10.1016/j.compeleceng.2017.02.002
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, surveillance is fast becoming an essential part of everyday life in both commercial and residential environments due to the vital role it plays in the society. The existing tracking methods are limited by factors such as illumination variations, occlusions, camera movements, and background clutters among others. Hence, a robust multi technique tracking method based on continuous adaptive mean shift (CamShift), corrected background weighted histogram, and unscented particle filter techniques is proposed. The mean square error statistic was used to evaluate the performance of the proposed method in terms of correctly estimating the path of a target object in video sequence acquired from three different scenarios. The obtained results showed that our proposed method has a significant improvement both quantitatively and qualitatively across all scenarios compared to the traditional CamShift and some existing methods. The proposed multi-technique approach might potentially improve the tracking capability of surveillance devices. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:557 / 568
页数:12
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