结合特征筛选与二次定位的快速压缩跟踪算法

耿磊; 王学彬; 肖志涛; 张芳; 吴骏; 李月龙; 苏静静

doi:10.16383/j.aas.2016.c150603

结合特征筛选与二次定位的快速压缩跟踪算法

doi: 10.16383/j.aas.2016.c150603

耿磊^1,2,,
王学彬^1,,
肖志涛^1,2, ,,
张芳^1,2,,
吴骏^1,2,,
李月龙^2,,
苏静静^1,

1.
天津工业大学电子与信息工程学院天津 300387
2.
天津市光电检测技术与系统重点实验室天津 300387

基金项目:

高等学校博士学科点专项科研基金 20131201110001

国家自然科学基金 61302127

天津市科技支撑计划重点项目 14ZCZDGX00033

详细信息

作者简介:
耿磊天津工业大学电子与信息工程学院副教授.2012年获得天津大学精密仪器与光电子工程学院博士学位.主要研究方向为图像处理与模式识别, 智能信号处理技术与系统, DSP系统研发.E-mail:genglei@tjpu.edu.cn

王学彬天津工业大学电子与信息工程学院教授. 2003年获得天津大学电子信息工程学院博士学位.主要研究方向为智能信号处理, 图像处理与模式识别.本文通信作者. E-mail: xiaozhitao@tjpu.edu.cn

张芳：ZHANG Fang Associate professor at the School of Electronics and Information Engineering, Tianjin Polytechnic University. She received her Ph.D. degree from the School of Precision Instrument and Opto-Electronics Engineering, Tianjin University in 2009. Her research interest covers image processing and pattern recognition, and optical interference measurement technique. E-mail: hhzhangfang@126.com

吴骏：WU Jun Associate professor at the School of Electronics and Information Engineering, Tianjin Polytechnic University. He received his Ph.D. degree from the School of Electronics and Information Engineering, Tianjin University in 2007. His research interest covers image processing and pattern recognition, and artificial neural network. E-mail: zhenkongwujun@163.com

李月龙：LI Yue-Long Associate professor at the School of Computer Science and Software Engineering, Tianjin Polytechnic University. He received his Ph.D. degree in computer science from the School of Electronics Engineering and Computer Science, Peking University in 2012. He was an academic visitor in the University of York in 2015. His research interest covers computer vision, pattern recognition, shape extraction, and face recognition. E-mail: liyuelong@pku.edu.cn

苏静静：SU Jing-Jing Master student at the School of Electronics and Information Engineering, Tianjin Polytechnic University. She received her bachelor degree from North China Institute of Aerospace Engineering in 2014. Her research interest covers pattern recognition, and machine learning. E-mail: 1065250074@qq.com

通讯作者:
肖志涛天津工业大学电子与信息工程学院教授.2003年获得天津大学电子信息工程学院博士学位.主要研究方向为智能信号处理, 图像处理与模式识别.本文通信作者.E-mail:xiaozhitao@tjpu.edu.cn

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出版历程
- 收稿日期: 2015-10-08
- 录用日期: 2016-01-23
- 刊出日期: 2016-09-01

Fast Compressive Tracking Algorithm Combining Feature Selection with Secondary Localization

GENG Lei^{1,2
,},
WANG Xue-Bin^1
,,
XIAO Zhi-Tao^{1,2
, ,},
ZHANG Fang^{1,2
,},
WU Jun^{1,2
,},
LI Yue-Long^2
,,
SU Jing-Jing^1
,

1.
School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387
2.
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387

Funds:

Specialized Research Fund for the Poctoral Program of Higher Education of China 20131201110001

National Natural Science Foundation of China 61302127

Key Projects of Tianjin Science and Technology Support Program 14ZCZDGX00033

More Information

Author Bio:
Associate professor at the School of Electronics and Information Engineering, Tianjin Polytechnic University. He received his Ph.D. degree from the School of Precision Instrument and Opto-Electronics Engineering, Tianjin University in 2012. His research interest covers image processing and pattern recognition, intelligent signal processing technology and system, DSP system research and development.E-mail:

Master student at the School of Electronics and Information Engineering, Tianjin Polytechnic University. He received his bachelor degree from the School of Electronics and Information Engineering, Tianjin Polytechnic University in 2013. His research interest covers pattern recognition and machine learning. E-mail:

天津工业大学电子与信息工程学院副教授. 2009年获得天津大学精密仪器与光电子工程学院博士学位.主要研究方向为图像处理与模式识别, 光学干涉测量技术. E-mail:

天津工业大学电子与信息工程学院副教授. 2007年获得天津大学电子信息工程学院博士学位.主要研究方向为图像处理与模式识别, 人工神经网络. E-mail:

Corresponding author: XIAO Zhi-Tao Professor at the School of Electronics and Information Engineering, Tianjin Polytechnic University. He received his Ph.D. degree from the School of Electronics and Information Engineering, Tianjin University in 2003. His research interest covers intelligent signal processing, image processing and pattern recognition. Corresponding author of this paper. E-mail:xiaozhitao@tjpu.edu.cn

摘要

摘要: 针对压缩跟踪算法易受遮挡影响和模型更新比较盲目的问题，提出结合特征筛选与二次定位的快速压缩跟踪算法（Fast compressive tracking algorithm combining feature selection with secondary localization，FSSL-CT）.首先，对全局区域划分子区域，从中提取压缩特征，根据正、负样本估计出各个压缩特征在正、负类中的分布; 然后，使用自适应学习率结合正类更新阈值对分类器模型进行更新; 最后，将跟踪分为两个阶段，每个阶段在对应的搜索区域内采集候选样本，并从全部特征中筛选出部分优质特征加权构建分类器，通过分类候选样本最终完成目标跟踪.在8个公共测试序列和4个自制序列中与最近提出的两个代表性算法进行比较，本文算法在大多数测试序列中都具有最高的跟踪成功率和最低的平均中心误差，平均处理速度可以达到3.04毫秒/帧.实验结果表明，本文算法具有更好的抵抗短时遮挡的能力，更高的准确性和鲁棒性，以及良好的实时性.
- 压缩跟踪 /
- 特征筛选 /
- 二次定位 /
- 分布差异 /
- 自适应更新
Abstract: As the traditional compressive tracking algorithm fails to track targets stably under occlusive condition and update model accurately, a fast tracking algorithm combining feature selection with secondary localization based on compressive tracking (FSSL-CT) is proposed. Firstly, compressive features are extracted from sub-regions partitioned from the global region, and the distributions of each compressive feature in positive and negative classes are estimated. Secondly, the classifier model is updated utilizing the method of adaptive learning rate and positive class update threshold. Finally, the tracking stage is divided into two procedures. In each procedure, some candidate samples are collected in the given searching region, and partial high quality features are selected from all the features and weighted to construct a classifier, then, the candidate samples are classified by the classifier. After that, the target tracking is achieved. Compared with two state-of-the-art algorithms on 8 public testing sequences and 4 private sequences, the FSSL-CT algorithm is proved to have the highest tracking success ratio and the lowest average central error in most of the sequences, and the average processing speed could achieve 3.04 milliseconds per frame. It is tested that the proposed FSSL-CT algorithm has a better capacity of resisting short-time occlusion and running in real-time, higher accuracy and robustness than the two state-of-the-art algorithms.
- Compressive tracking /
- feature select /
- secondary localization /
- distribution difference /
- adaptive update

HTML全文

图 1 降维示意图

Fig. 1 The diagram of dimensionality reduction

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图 2 B与分布的关系

Fig. 2 Relationship between B and distribution

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图 3 子区域模板

Fig. 3 Sub-region template

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图 4 二次定位示意图

Fig. 4 The diagram of secondary localization

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图 5 精确度曲线

Fig. 5 Precision plot

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图 6 Sylvester (第4、697、1184、1236帧)

Fig. 6 Sylvester (Frames 4、697、1184、1236

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图 7 Jogging (第53、78、79、231帧)

Fig. 7 Jogging (Frames 53、78、79、231)

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图 8 Tiger1 (第7、64、115、315帧)

Fig. 8 Tiger1 (Frames 7、64、115、315)

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图 9 Hat (第36、295、429、515帧)

Fig. 9 Hat (Frames 36、295、429、515)

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图 10 DayLight1 (第71、114、197、543帧)

Fig. 10 DayLight1 (Frames 71、114、197、543)

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表 1 各算法跟踪成功率比较

Table 1 Comparison of the tracking success rate of different algorithms

视频序列	FSSL-CT (\%)	CT/原文(\%)	FCT/原文(\%)
Jogging	95.76	22.47/—	22.14/—
Girl	85.00	65.40/78	52.20/—
Shaking	81.91	85.20/92	93.42/97
FleetFace	90.24	76.80/—	67.60/—
KiteSurf	98.80	70.23/68	57.14/—
Tiger1	71.75	64.40/78	52.82/52
Sylvester	86.39	80.29/75	69.29/77
FaceOcc2	90.02	96.55/100	89.16/—
Hat	98.00	83.04/—	89.90/—
DayLight1	100.00	48.90/—	76.55/—
DayLight2	100.00	91.78/—	100.00/—
Night	100.00	77.56/—	100.00/—

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表 2 各算法平均中心误差比较(像素)

Table 2 Comparison of mean center error of different algorithms (pixel)

视频序列	FSSL-CT	CT/原文	FCT/原文
Jogging	12.07	94.48/—	93.59/—
Girl	5.66	8.31/21	12.74/—
Shaking	11.78	13.20/9	10.70/14
FleetFace	16.70	52.50/—	54.72/—
KiteSurf	2.24	6.12/9	13.93/—
Tiger1	22.40	25.35/10	35.59/23
Sylvester	9.94	16.56/9	20.70/9
FaceOcc2	12.57	11.78/10	16.10/—
Hat	8.81	18.09/—	11.19/—
DayLight1	9.89	25.70/—	19.54/—
DayLight2	6.69	15.91/—	8.90/—
Night	6.16	14.00/—	9.53/—

下载: 导出CSV

表 3 各算法计算次数及相关参数(次)

Table 3 Calculation times and related parameters of different algorithms (times)

参数	FSSL-CT	CT/原文	FCT/原文
m₁	81	1961	121
n₁	40	50	100
m₂	81	0	317
n₂	80	0	100
N	9720	98050	43800

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表 4 各算法运行速度比较(毫秒/帧)

Table 4 Comparison of operation speed of different algorithms (ms/frame)

视频序列	FSSL-CT	CT	FCT
Jogging	3.04	18.10	11.34
Girl	2.63	18.85	11.20
Shaking	3.31	19.95	12.39
FleetFace	3.09	21.35	12.45
KiteSurf	2.97	21.39	11.89
Tiger1	3.41	21.57	12.85
Sylvester	2.72	20.86	12.36
FaceOcc2	3.06	21.38	12.75
Hat	2.88	19.24	11.43
DayLight1	3.17	20.26	11.49
DayLight2	3.26	21.43	11.92
Night	2.98	21.36	11.76
平均处理时间	3.04	20.48	11.99

下载: 导出CSV

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结合特征筛选与二次定位的快速压缩跟踪算法

doi: 10.16383/j.aas.2016.c150603

通讯作者:
肖志涛天津工业大学电子与信息工程学院教授.2003年获得天津大学电子信息工程学院博士学位.主要研究方向为智能信号处理, 图像处理与模式识别.本文通信作者.E-mail:xiaozhitao@tjpu.edu.cn

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Fast Compressive Tracking Algorithm Combining Feature Selection with Secondary Localization

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留言板

结合特征筛选与二次定位的快速压缩跟踪算法

doi: 10.16383/j.aas.2016.c150603

通讯作者: 肖志涛 天津工业大学电子与信息工程学院教授.2003年获得天津大学电子信息工程学院博士学位.主要研究方向为智能信号处理, 图像处理与模式识别.本文通信作者.E-mail:xiaozhitao@tjpu.edu.cn

计量

出版历程

Fast Compressive Tracking Algorithm Combining Feature Selection with Secondary Localization

计量

出版历程

目录

通讯作者:
肖志涛天津工业大学电子与信息工程学院教授.2003年获得天津大学电子信息工程学院博士学位.主要研究方向为智能信号处理, 图像处理与模式识别.本文通信作者.E-mail:xiaozhitao@tjpu.edu.cn