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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (10): 1834-1846    DOI: 10.3785/j.issn.1008-973X.2021.10.005
    
Adaptive context-aware target tracking method
Yun-xu BAI1(),Xin-jiang LU1,*(),Rui LUO2
1. College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
2. Hunan Industry Polytechnic, Changsha 410083, China
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Abstract  

An adaptive context-aware target tracking method was proposed aiming at the problem that the correlation filtering methods were easily interfered by background, which led to tracking drift. The high response area far from the target position was selected as the adaptive context area in order to reduce the background interference. Then the adaptive low response value was assigned to the area. The penalty factor was adaptively given to the context area according to the relative difference of response value between the context area and the target area, which made the algorithm more robust. The algorithm showed excellent tracking performance on OTB2013, OTB2015 and Temple-Color128 benchmark. The overlapping rate accuracy of OTB2015 was 61.53%, which was superior to most existing excellent algorithms. The algorithm performed better especially in the case of background clutter and partial occlusion. The average tracking speed of the algorithm was 24.5 frames per second, and the algorithm had a good real-time effect.

Key words： correlation filtering      target tracking      adaptive context-aware      background interference      tracking drift     
Received: 03 November 2020      Published: 27 October 2021
CLC:  TP 391  
Fund:  国家重点研发计划资助项目（2018YFB1308202）；国家自然科学基金资助项目（52075556）；湖南省杰青资助项目（2019JJ20030）；湖南省高新技术产业科技创新引领计划资助项目（2020GK4097）；湖南省教育厅科学研究资助项目（12B035）
Corresponding Authors: Xin-jiang LU     E-mail: yxbai2017@csu.edu.cn;xjlu@csu.edu.cn
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Yun-xu BAI
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Cite this article:

Yun-xu BAI,Xin-jiang LU,Rui LUO. Adaptive context-aware target tracking method. Journal of ZheJiang University (Engineering Science), 2021, 55(10): 1834-1846.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.10.005     OR     https://www.zjujournals.com/eng/Y2021/V55/I10/1834


自适应上下文感知的目标追踪方法

针对相关滤波方法容易受到背景干扰导致跟踪漂移的问题，提出自适应上下文感知图像跟踪方法. 为了减少背景干扰，选取离目标位置远的高响应区域为自适应上下文区域，赋予该区域自适应的低响应值. 根据上下文区域与目标区域响应的相对差值，给上下文区域自适应的惩罚因子，使得该算法具有更好的鲁棒性. 该算法在OTB2013、OTB2015及Temple-Color128标准数据集上都展现了优秀的跟踪性能，OTB2015的重叠率精度达到61.53%，超过大部分已有的优秀算法，特别是在背景混叠及部分遮挡的情况下有着更卓越的表现. 该算法的平均跟踪速度为24.5帧/s，实时性较好.


关键词： 相关滤波,  目标追踪,  自适应上下文感知,  背景干扰,  跟踪漂移 
Fig.1 Schematic diagram of traditional context awareness
Fig.2 Adaptive selection of context region
Fig.3 Context area response value
Fig.4 Adaptive penalty factor
Fig.5 Flow chart of adaptive context-aware algorithm
Fig.6 Comparison of DP accuracy and AUC success rate between traditional CACF algorithm and proposed algorithm under different K value
算法 DP /% OP /% AUC /% v /（帧·s?1）
ACACF（K=1） 85.72 72.63 60.39 25.8
ACACF（K=2） 86.87 74.74 61.53 24.5
ACACF（K=3） 85.43 72.21 60.10 23.8
ACACF（K=4） 79.73 67.79 56.81 21.8
CACF 80.11 70.24 58.56 25.7
Tab.1 Scores of each evaluation index under different K values
算法 DP /% OP /% AUC /% v /（帧·s?1）
ADA_CA（K=2） 88.16 81.12 64.29 27.2
BACF 83.87 81.41 63.78 27.1
ARCF 82.84 79.18 62.62 4.5
Staple 79.26 75.39 59.95 44.9
SRDCF 83.79 78.13 62.62 4.5
CSRDCF 80.28 73.78 59.33 22.8
LMCF 84.20 80.03 62.76 77.6
LCT 84.12 80.70 62.37 21.6
SAMF 78.50 73.19 57.93 18.6
fDSST 73.97 67.03 55.42 19.4
CFNet 78.49 75.19 58.89 NaN
SiamFC 80.93 77.86 60.73 NaN
DCFNet 79.45 77.86 62.24 21.2
ACFN 85.96 75.03 60.71 7.0
HCF 87.86 73.22 59.75 8.0
HDT 87.74 72.96 59.64 4.3
Tab.2 Scores of each evaluation index between outstanding algorithms on OTB2013 database
Fig.7 Comparison of DP accuracy and AUC success rate between proposed algorithm and outstanding tracking algorithm on OTB2013 database
算法 DP /% OP /% AUC /% v /（帧·s?1）
ADA_CA（K=2） 86.87 76.34 61.53 24.5
BACF 81.06 76.74 61.09 27.5
ARCF 80.65 74.65 60.66 24.4
Staple 78.40 70.92 58.13 42.9
SRDCF 78.95 72.84 59.80 4.3
CSRDCF 80.24 70.19 58.69 22.6
LMCF 78.85 71.88 58.01 42.9
LCT 75.84 69.77 55.95 20.7
SAMF 75.13 67.37 55.32 17.0
fDSST 68.67 60.53 51.73 17.7
CFNet 77.71 73.67 58.62 NaN
SiamFC 77.05 73.05 58.21 NaN
DCFNet 75.07 41.20 57.99 41.2
ACFN 79.90 69.25 57.31 10.0
HCF 83.10 65.14 55.81 10.4
HDT 84.20 65.35 56.11 5.5
Tab.3 Scores of each evaluation index between outstanding algorithms on OTB2015 database
Fig.8 Comparison of DP accuracy and AUC success rate between proposed algorithm and outstanding tracking algorithm on OTB2015 database
算法 DP /% OP /% AUC /% v /（帧·s?1）
ADA_CA（K=2） 72.81 66.01 53.41 21.1
BACF 64.58 61.30 48.64 37.6
ARCF 70.27 64.59 51.94 22.2
Staple 66.66 62.01 49.71 83.2
SRDCF 68.91 61.30 50.53 2.8
CSRDCF 67.74 59.19 50.04 16.6
MEEM 70.87 61.16 49.77 20.5
LCT 60.59 52.56 43.17 39.3
SAMF 63.07 57.88 46.56 27.1
fDSST 53.34 46.95 40.50 16.3
RCT 72.60 65.60 52.95 34.3
WSCF 69.98 63.90 51.08 24.5
TRACF 71.33 64.81 52.21 23.7
TSC 72.09 66.50 53.34 2.2
DAMA 67.69 61.01 49.72 30.4
PSCA 71.03 63.23 51.33 1.8
Tab.4 Scores of each evaluation index between outstanding algorithms on TC128 database
Fig.9 Comparison of DP accuracy and AUC success rate between proposed algorithm and outstanding tracking algorithm on TC128 database
Fig.10 Visual tracking performance on complex video sequences (Human 3, Girl, Soccer, DragonBaby, iroman)
Fig.11 Success plots of outstanding algorithm with 11 video attributes on OTB2015 dataset.
Fig.12 Failure case (carScale, freeman4, matrix, motorRolling)
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