基于改进亮度变化函数实现红外图像中行人跟踪

郑超; 陈杰; 陶会峰; 殷松峰; 杨星; 王一程

doi:10.3788/OPE.20152310.2980

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基于改进亮度变化函数实现红外图像中行人跟踪

信息科学 | 更新时间：2020-08-13

- 基于改进亮度变化函数实现红外图像中行人跟踪
- Pedestrian tracking in FLIR imagery based on modified intensity variation function
- 光学精密工程 2015年23卷第10期页码：2980-2988
- 作者机构：
  
  1. 解放军电子工程学院脉冲功率激光技术国家重点实验室红外与低温等离子体安徽省重点实验室,安徽合肥,230037
  2. 安徽建筑大学电子与信息工程学院,安徽合肥,230037
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61503394)();安徽省自然科学基金资助项目(No.1408085QF131,No.1508085QF121)();安徽高校自然科
- DOI：10.3788/OPE.20152310.2980
  中图分类号： TP391.41
- 收稿日期：2015-06-10，
  
  修回日期：2015-08-11，
  
  纸质出版日期：2015-10-25
- 稿件说明：
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郑超, 陈杰, 陶会峰等. 基于改进亮度变化函数实现红外图像中行人跟踪[J]. 光学精密工程, 2015,23(10): 2980-2988

ZHENG Chao, CHEN Jie, TAO Hui-feng etc. Pedestrian tracking in FLIR imagery based on modified intensity variation function[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10): 2980-2988
郑超, 陈杰, 陶会峰等. 基于改进亮度变化函数实现红外图像中行人跟踪[J]. 光学精密工程, 2015,23(10): 2980-2988 DOI： 10.3788/OPE.20152310.2980.

ZHENG Chao, CHEN Jie, TAO Hui-feng etc. Pedestrian tracking in FLIR imagery based on modified intensity variation function[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10): 2980-2988 DOI： 10.3788/OPE.20152310.2980.

摘要

由于基于亮度变化函数(IVF)的跟踪算法能高效跟踪前视红外图像中刚性目标但无法满足行人跟踪鲁棒性要求

提出了一种新的基于多热点亮度变化函数的红外图像中行人跟踪算法。分析了分区域、多热点描述行人目标热信号的必要性

利用改进的亮度变化函数在帧间目标窗口内定位热点

建立目标窗口自适应更新机制解决尺度变化问题

最后基于热点的运动特征描述子剔除定位于背景的野值点。对复杂红外场景的跟踪实验结果表明

由于在原始算法的基础上省去了模板匹配步骤及缩小了搜索对象的矩阵维数

该算法获得了最优的实时性;且多热点机制使该算法的鲁棒性优于多种其他视觉跟踪算法

能够胜任存在遮挡、尺度变化、低对比度等干扰因素的前视红外图像中行人目标的跟踪。

Abstract

As tracking algorithms based on Intensity Variation Function(IVF) can track effectively rigid targets in Forward Look Infrared(FLIR) imagery

but can not satisfy the need of a pedestrian tracking for robustness

a novel pedestrian tracking algorithm based on modified IVF was proposed. The necessity of describing the thermal signatures of pedestrians with multiple hot spots in divided subregions was analyzed. Then

the hot spots were detected in a target window from frame to frame by the modified IVF and an adaptive update mechanism for a target window was established to solve the scale change. Finally

the motion feature descriptors based on hot spots were used to remove the outliers detected unaccurately in the background. Comparative experiments on challenging thermal scenes demonstrate that the proposed algorithm outperforms the state-of-the-art approaches in real-time performance by removing the template matching step of original algorithm and decreasing the matrix dimension of searching objects. Moreover

by better robustness against many visual tracking algorithms with the multiple hot spot strategy

it is suitable for the pedestrian tracking in FLIR imagery with the interference of occlusion

scale changed and lower contrast.

关键词

Keywords

references

陈东成,朱明,高文,等. 在线加权多示例学习实时目标跟踪[J]. 光学精密工程, 2014, 22(6): 1661-1667. CHEN D CH, ZHU M, GAO W, et al.. Real-time object tracking via online weighted multiple instance learning[J]. Opt. Precision Eng., 2014, 22(6):1661-1667. (in Chinese)

龚俊亮,何昕,魏仲慧,等. 采用改进辅助粒子滤波的红外多目标跟踪[J]. 光学精密工程,2012,20(2):413-421. GONG J L, HE X, WEI ZH H, et al.. Multiple infrared target tracking using improved auxiliary particle filter[J]. Opt. Precision Eng.,2012,20(2):413-421.(in Chinese)

王江涛,陈得宝,李素文,等. 在线自适应选择子空间的红外目标跟踪方法[J]. 红外与激光工程, 2013, 42(9): 2579-2583. WANG J T, CHEN D B, LI S W, et al.. IR object tracking method via online adaptive subspace selection [J]. Infrared and Laser Engineering, 2013, 42(9):2579-2583. (in Chinese)

郭敬明,何昕,杨杰,等. 模板自适应的 Mean Shift 红外目标跟踪[J]. 红外与激光工程, 2014, 43(4): 1087-1093. GUO J M, HE X, YANG J, et al.. Infrared target tracking based on template adaptive Mean Shift[J]. Infrared and Laser Engineering, 2014, 43(4): 1087-1093. (in Chinese)

赵文达,赵建,韩希珍,等. 基于变分偏微分方程的红外图像增强算法研究[J]. 液晶与显示,2014,29(2):281-285. ZHAO W D,ZHAO J,HAN X ZH,et al..Infrared image enhancement based on variational partial differential equations[J].Chinese Journal of Liquid Crystals and Displays,2014,29(2):281-285.(in Chinese)

高文,朱明,刘剑,等. 基于DSP+FPGA框架的实时目标跟踪系统设计[J]. 液晶与显示,2014,29(4):611-616. GAO W,ZHU M,LIU J,et al..Design of real-time target tracking system based on DSP+FPGA[J].Chinese Journal of Liquid Crystals and Displays,2014,29(4):611-616.(in Chinese)

陈广秋,高印寒,段锦,等. 基于奇异值分解的PCNN红外与可见光图像融合[J]. 液晶与显示,2015,30(1):126-136. CHEN G Q,GAO Y H,DUAN J,et al..Fusion algorithm of infrared and visible images based on singular value decomposition and PCNN[J].Chinese Journal of Liquid Crystals and Displays,2015,30(1):126-136.(in Chinese)

BAI A, ALAM M S. Automatic target tracking in FLIR image sequences using intensity variation function and template modeling[J]. IEEE Transactions on Instrumentation and Measurement, 2005, 54(5): 1846-1852.

PARAVATI G, SANNA A, PRALIO B, et al.. A genetic algorithm for target tracking in FLIR video sequences using intensity variation function[J].IEEE Transactions on Instrumentation and Measurement, 2009, 58(10): 3457-3467.

PARAVATI G, ESPOSITO S. Relevance-based template matching for tracking targets in FLIR imagery[J]. Sensors, 2014, 14(8): 14106-14130.

LAMBERTI F, SANNA A, PARAVATI G. Improving robustness of infrared target tracking algorithms based on template matching[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 1467-1480.

LAMBERTI F, SANNA A, PARAVATI G, et al.. IVF3: exploiting intensity variation function for high-performance pedestrian tracking in forward-looking infrared imagery [J]. Optical Engineering, 2014, 53(2): 023105-023105.

LAMBERTI F, SANTOMO R, SANNA A, et al.. Intensity variation function and template matching-based pedestrian tracking in infrared imagery with occlusion detection and recovery[J]. Optical Engineering, 2015, 54(3): 033106.

Terravic Outdoor House Surveillance IR Database(2014-05-20). http://www.terravic.com/research/mot-ion.htm.

OTCBVS Benchmark Dataset Collection[EB]. (2014-05-20). http://www.cse.ohio-state.edu/otcbvs-bench/.

WU Y, LIM J, YANG M H. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition 2013, 2013: 2411-2418.

ZHANG K, ZHANG L, YANG M H. Real-time compressive tracking[C].Computer Vision-ECCV. Springer Berlin Heidelberg, 2012: 864-877.

ORON S, BAR-HILLEL A, LEVI D, et al.. Locally orderless tracking[C]. IEEE Conference on Computer and Pattern Recognition, 2012:1940-1947.

SEVILLA-LARA L, LEARNED-MILLER E. Distribution fields for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012: 1910-1917.

BAO C, WU Y, LING H, et al.. Real time robust l1 tracker using accelerated proximal gradient approach[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012: 1830-1837.

ZHANG T, GHANEM B, LIU S, et al.. Robust visual tracking via multi-task sparse learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2042-2049.

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