结合分段复合权值与多策略的视觉运动目标跟踪

苏延召; 李艾华; 王涛; 张玮; 金广智

doi:10.3788/OPE.20142212.3409

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结合分段复合权值与多策略的视觉运动目标跟踪

信息科学 | 更新时间：2020-08-13

- 结合分段复合权值与多策略的视觉运动目标跟踪
- Visual tracking of moving objects based on piecewise fusion weight and multi-strategy
- 光学精密工程 2014年22卷第12期页码：3409-3418
- 作者机构：
  
  第二炮兵工程大学502教研室,陕西西安,710025
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年基金资助项目(61201449)()
- DOI：10.3788/OPE.20142212.3409
  中图分类号： TP391
- 收稿日期：2014-08-04，
  
  修回日期：2014-10-01，
  
  纸质出版日期：2014-12-25
- 稿件说明：
移动端阅览
苏延召, 李艾华, 王涛等. 结合分段复合权值与多策略的视觉运动目标跟踪[J]. 光学精密工程, 2014,22(12): 3409-3418

SU Yan-zhao, LI Ai-hua, WANG Tao etc. Visual tracking of moving objects based on piecewise fusion weight and multi-strategy[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3409-3418
苏延召, 李艾华, 王涛等. 结合分段复合权值与多策略的视觉运动目标跟踪[J]. 光学精密工程, 2014,22(12): 3409-3418 DOI： 10.3788/OPE.20142212.3409.

SU Yan-zhao, LI Ai-hua, WANG Tao etc. Visual tracking of moving objects based on piecewise fusion weight and multi-strategy[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3409-3418 DOI： 10.3788/OPE.20142212.3409.

摘要

由于视觉监控中运动目标跟踪的准确性易受遮挡、摄像机运动、目标外观变化等因素的影响

本文提出了一种结合分段复合权值与多策略的视觉跟踪算法.该算法首先利用目标、背景以及候选区域特征信息建立分段的复合权值得到目标的位置概率分布.然后结合空间一致性和滞后阈值分割目标位置概率图以进一步抑制噪声干扰

同时通过分析分段复合权值变化判断目标遮挡

调整目标跟踪候选范围

并结合目标历史尺度信息对当前目标尺度进行自适应调整.最后

对目标以及背景区域信息进行动态更新以适应目标外观与场景变化.与典型算法进行的对比实验结果表明:该算法能够有效地应对目标遮挡与摄像机运动等因素的影响

实验时对各组视频的平均处理时间约为10 ms左右

适用于复杂场景条件下运动目标的实时跟踪.

Abstract

As the accuracy of moving object tracking in video surveillance is disturbed by occlusion

camera moving and target appearance changing

an algorithm based on piecewise fusion weight and multi-strategy was proposed. Firstly

the piecewise fusion weight was constructed by combining the feature of object

background and candidate regions to obtain the likelihood image of object location. Then

the likelihood image was segmented with the spatial coherent and hysteresis threshold to suppress noise interference. Meanwhile

the object occlusion was determined and handled by analyzing the change of the piecewise fusion weight and enlarging the candidate area. Furthermore

the object scale was adaptively adjusted according to history and current scales. Finally

object information and background regions were dynamically updated to adapt to the object appearance and scene changing. Experimental results compared with other traditional methods show that the proposed algorithm is applicable to process the moving object tracking in low-contrast scenes in real time

and the average processing time for different video images is 10 ms

which means that the algorithm is suitable for the moving object tracking in complex scenes.

关键词

Keywords

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