一种新的FAST-Snake目标跟踪方法

王蒙; 戴亚平; 王庆林

doi:10.3724/SP.J.1004.2014.01108

一种新的FAST-Snake目标跟踪方法

doi: 10.3724/SP.J.1004.2014.01108

1.
北京理工大学自动化学院北京 100081;
2.
大理学院数学与计算机学院大理 671003

详细信息

作者简介:
戴亚平北京理工大学自动化学院教授.主要研究方向为机动目标跟踪，基于网络的远程控制，多传感器数据融合.E-mail：daiyaping.bit@gmail.com

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出版历程
- 收稿日期: 2013-04-23
- 修回日期: 2013-09-22
- 刊出日期: 2014-06-20

A Novel FAST-Snake Object Tracking Approach

1.
School of Automation, Beijing Institute of Technology University, Beijing 100081;
2.
School of Mathematics and Computer Science, Dali University, Dali 671003

摘要

摘要: 提出一种新的FAST-Snake目标跟踪方法，利用改进的FAST角点特征匹配来估计目标轮廓在帧间的全局仿射变换，将投影轮廓点作为Snake模型的初始化轮廓.为提高跟踪实时性，在Snake能量模型中定义了先验约束能，并用限定搜索方向的贪婪算法（Greedy algorithm）实现局部轮廓优化.实验包括三维目标数据库及真实场景视频，验证了提出方法的均方误差（Means quare error，MSE）及收敛速度评估均优于对比算法，并具备对复杂运动及局部遮挡的适应能力.
- FAST-Snake方法 /
- Snake模型 /
- 特征点匹配 /
- 主动轮廓 /
- 目标跟踪
Abstract: We present a novel FAST-Snake tracking approach using improved FAST-feature matching to estimate affine transform of contour points between frames as the initial contour of the Snake model. For real-time tracking, we define a prior constraint energy in the Snake model and adopt the greedy algorithm to implement contour optimization. Experiments involving 3-D object database and video sequences show that the proposed approach is superior to its counterpart in terms of mean square error (MSE) and convergence speed, and that it has the adaptability to complex motion and partial occlusion.
- FAST-Snake approach /
- Snake models /
- feature point matching /
- active contours /
- object tracking

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参考文献(20)

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