Tracking video objects with feature points based particle filtering

被引：23

作者：

Gao, Tao ^{[1
,2
]}

Li, Guo ^{[3
]}

Lian, Shiguo ^{[4
]}

Zhang, Jun ^{[5
]}

机构：

[1] Elect Informat Prod Supervis & Inspect Inst Hebei, Shijiazhuang 050071, Peoples R China

[2] Ind & Informat Technol Dept Hebei Prov, Shijiazhuang 050051, Peoples R China

[3] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China

[4] France Telecom Orange Labs Beijing, Beijing 100080, Peoples R China

[5] Tianjin Univ, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2012年 / 58卷 / 01期

基金：

美国国家科学基金会;

关键词：

Moving objects tracking; Motion detection; SIFT; Particle filtering; Video surveillance; MEAN SHIFT; ROBUST;

D O I：

10.1007/s11042-010-0676-y

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems.

引用

页码：1 / 21

页数：21

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