Hybrid multiple-object tracker incorporating Particle Swarm Optimization and Particle Filter

Chen-Chien James Hsu, Yung Ching Chu, Ming Chih Lu

研究成果: 書貢獻/報告類型會議論文篇章

摘要

This study presents a hybrid algorithm incorporating Particle Swarm Optimization (PSO) and Particle Filter (PF) for multiple-object tracking based mainly on gray-level histogram model. To start with, the hybrid object tracker uses PSO to search the objects in the beginning, taking advantage of the PSO for global optimization. Once the objects have been successfully found by PSO, the hybrid object tracker then switches to PF to continuously track the objects. To avoid the varying-size problem of the objects, Speeded Up Robust Features (SURF) is used to detect the object around its neighborhood in the video sequence for defining the real image size of the object for remodeling the target object by histogram. As a result, tracking speed can be maintained by the hybrid tracker using simple histogram model while circumventing the varying-size problem of the objects during the tracking process.

原文英語
主出版物標題ICSSE 2013 - IEEE International Conference on System Science and Engineering, Proceedings
頁面189-193
頁數5
DOIs
出版狀態已發佈 - 2013 十一月 18
事件IEEE International Conference on System Science and Engineering, ICSSE 2013 - Budapest, 匈牙利
持續時間: 2013 七月 42013 七月 6

出版系列

名字ICSSE 2013 - IEEE International Conference on System Science and Engineering, Proceedings

其他

其他IEEE International Conference on System Science and Engineering, ICSSE 2013
國家/地區匈牙利
城市Budapest
期間2013/07/042013/07/06

ASJC Scopus subject areas

  • 控制與系統工程

指紋

深入研究「Hybrid multiple-object tracker incorporating Particle Swarm Optimization and Particle Filter」主題。共同形成了獨特的指紋。

引用此