计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (3): 649-660.DOI: 10.3778/j.issn.1673-9418.2010029
收稿日期:
2020-10-12
修回日期:
2020-11-30
出版日期:
2022-03-01
发布日期:
2020-12-08
通讯作者:
+ E-mail: xie_linbo@jiangnan.edu.cn作者简介:
程世龙(1995—),男,安徽宿州人,硕士研究生,主要研究方向为目标跟踪、深度学习。基金资助:
CHENG Shilong, XIE Linbo+(), PENG Li
Received:
2020-10-12
Revised:
2020-11-30
Online:
2022-03-01
Published:
2020-12-08
About author:
CHENG Shilong, born in 1995, M.S. candidate. His research interests include visual object track-ing and deep learning.Supported by:
摘要:
通常在目标跟踪任务中需要跟踪的目标物体具有任意性,同时目标周围可能有相似的干扰物体,这常常导致预训练网络提取的目标特征并不完全适用于当前需要跟踪的目标物体。针对以上问题,在Siamese孪生网络目标跟踪框架下,提出一种新型的基于梯度导向的通道选择目标跟踪算法。首先从预训练网络提取待跟踪目标特征,利用提出的开关-惩罚损失函数对背景中的相似性干扰物体施加惩罚操作,以排除相似物体对跟踪目标的干扰;其次在特征通道选择阶段,根据损失函数反向传播的梯度信息选择特征表达性最强的特征通道;最后在模板分支与搜索分支进行互相关操作部分,利用逐通道互相关方法获得加权的分数响应图以获得更精确的目标位置。在OTB和VOT公开数据集上将该算法和主流算法进行比较。实验结果表明,该算法具有良好的抗背景干扰能力和鲁棒性,在主要跟踪指标上达到了主流跟踪算法的性能。
中图分类号:
程世龙, 谢林柏, 彭力. 梯度导向的通道选择目标跟踪算法[J]. 计算机科学与探索, 2022, 16(3): 649-660.
CHENG Shilong, XIE Linbo, PENG Li. Gradient-Guided Object Tracking Algorithm with Channel Selection[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(3): 649-660.
算法 | OTB50 | OTB100 | ||
---|---|---|---|---|
精度 | 成功率 | 精度 | 成功率 | |
SRDCF[ | 0.731 | 0.539 | 0.789 | 0.598 |
DeepSRDCF[ | 0.772 | 0.560 | 0.851 | 0.635 |
Staple[ | 0.683 | 0.506 | 0.783 | 0.578 |
fDSST[ | 0.616 | 0.460 | 0.686 | 0.517 |
CFNet[ | 0.724 | 0.535 | 0.778 | 0.587 |
SiamFC[ | 0.693 | 0.519 | 0.772 | 0.587 |
SiamRPN[ | 0.806 | 0.583 | 0.847 | 0.629 |
DaSiamRPN[ | 0.829 | 0.604 | 0.880 | 0.658 |
SiamDWfc[ | 0.775 | 0.569 | 0.828 | 0.627 |
GradNet[ | 0.825 | 0.597 | 0.861 | 0.639 |
Proposed | 0.835 | 0.619 | 0.865 | 0.659 |
表1 OTB数据集上各算法的AUC值
Table 1 AUC of algorithms on OTB dataset
算法 | OTB50 | OTB100 | ||
---|---|---|---|---|
精度 | 成功率 | 精度 | 成功率 | |
SRDCF[ | 0.731 | 0.539 | 0.789 | 0.598 |
DeepSRDCF[ | 0.772 | 0.560 | 0.851 | 0.635 |
Staple[ | 0.683 | 0.506 | 0.783 | 0.578 |
fDSST[ | 0.616 | 0.460 | 0.686 | 0.517 |
CFNet[ | 0.724 | 0.535 | 0.778 | 0.587 |
SiamFC[ | 0.693 | 0.519 | 0.772 | 0.587 |
SiamRPN[ | 0.806 | 0.583 | 0.847 | 0.629 |
DaSiamRPN[ | 0.829 | 0.604 | 0.880 | 0.658 |
SiamDWfc[ | 0.775 | 0.569 | 0.828 | 0.627 |
GradNet[ | 0.825 | 0.597 | 0.861 | 0.639 |
Proposed | 0.835 | 0.619 | 0.865 | 0.659 |
算法 | A | EAO | R |
---|---|---|---|
UCT [ | 0.482 | 0.206 | 0.490 |
UNet-SiamFC[ | 0.490 | 0.214 | 0.580 |
SiamRPN[ | 0.490 | 0.244 | 0.460 |
ECO_HC[ | 0.494 | 0.177 | 0.571 |
SA-Siam[ | 0.500 | 0.236 | 0.459 |
SimpleRT-MDNet [ | 0.508 | 0.218 | 0.464 |
Proposed | 0.509 | 0.245 | 0.460 |
表2 VOT2018数据集上各跟踪算法性能比较
Table 2 Performance comparison of tracking algorithms on VOT2018 dataset
算法 | A | EAO | R |
---|---|---|---|
UCT [ | 0.482 | 0.206 | 0.490 |
UNet-SiamFC[ | 0.490 | 0.214 | 0.580 |
SiamRPN[ | 0.490 | 0.244 | 0.460 |
ECO_HC[ | 0.494 | 0.177 | 0.571 |
SA-Siam[ | 0.500 | 0.236 | 0.459 |
SimpleRT-MDNet [ | 0.508 | 0.218 | 0.464 |
Proposed | 0.509 | 0.245 | 0.460 |
序列 | 帧数 | 主要挑战 |
---|---|---|
Matrix | 100 | 遮挡、光照变化、快速运动、背景混乱 |
Bird1 | 408 | 遮挡、形变、快速运动、超出视野 |
Box | 1 161 | 遮挡、旋转、尺度变化、运动模糊 |
MotorRolling | 164 | 旋转、运动模糊、快速运动、背景混乱 |
Soccer | 392 | 遮挡、运动模糊、快速运动、背景混乱 |
表3 OTB100上部分视频序列属性
Table 3 Some video properties on OTB100
序列 | 帧数 | 主要挑战 |
---|---|---|
Matrix | 100 | 遮挡、光照变化、快速运动、背景混乱 |
Bird1 | 408 | 遮挡、形变、快速运动、超出视野 |
Box | 1 161 | 遮挡、旋转、尺度变化、运动模糊 |
MotorRolling | 164 | 旋转、运动模糊、快速运动、背景混乱 |
Soccer | 392 | 遮挡、运动模糊、快速运动、背景混乱 |
模块 | 精度 | 成功率 |
---|---|---|
Initial | 0.835 | 0.619 |
S-P | 0.860 | 0.648 |
S-P+M-C | 0.865 | 0.659 |
表4 OTB100数据集上算法各模块跟踪精度和跟踪成功率对比
Table 4 Tracking accuracy and success rate of each module on OTB100 dataset
模块 | 精度 | 成功率 |
---|---|---|
Initial | 0.835 | 0.619 |
S-P | 0.860 | 0.648 |
S-P+M-C | 0.865 | 0.659 |
[1] | 卢湖川, 李佩霞, 王栋. 目标跟踪算法综述[J]. 模式识别与人工智能, 2018, 31(1): 61-76. |
LU H C, LI P X, WANG D. Visual object tracking: a survey[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(1): 61-76. | |
[2] | 尹宽, 李均利, 李丽, 等. 复杂情况下自适应特征更新目标跟踪算法[J]. 光学学报, 2019, 39(11): 227-242. |
YIN K, LI J L, LI L, et al. Adaptive feature update object-tracking algorithm in complex situations[J]. Acta Optica Sinica, 2019, 39(11): 227-242. | |
[3] | MA C, HUANG J B, YANG X K, et al. Hierarchical convolu-tional features for visual tracking[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Dec 11-18, 2015. Washington: IEEE Computer Society, 2015: 3074-3082. |
[4] | ZHU Z, WU W, ZOU W, et al. End to end flow correlation tracking with spatial-temporal attention[C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Washington:IEEE Computer Society, 2018: 548-557. |
[5] | 刘芳, 黄光伟, 路丽霞, 等. 自适应模板更新的鲁棒目标跟踪算法[J]. 计算机科学与探索, 2019, 13(1): 83-96. |
LIU F, HUANG G W, LU L X, et al. Robust target tracking algorithm for adaptive template updating[J]. Journal of Fron-tiers of Computer Science and Technology, 2019, 13(1): 83-96. | |
[6] | BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional siamese networks for object tracking[C]// LNCS 9914: Proceedings of the 14th European Conference on Computer Vision, Amsterdam, Oct 8-10 and 15-16, 2016. Cham: Springer, 2016: 850-865. |
[7] | LI B, YAN J J, WU W, et al. High performance visual tracking with siamese region proposal network[C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Washington:IEEE Computer Society, 2018: 8971-8980. |
[8] | REN S Q, HE K M, GIRSHICK R B, et al. Faster R-CNN: towards real-time object detection with region proposal net-works[C]// Proceedings of the Annual Conference on Neural Information Processing Systems, Montreal, Dec 7-12, 2015. Red Hook: Curran Associates, 2015: 91-99. |
[9] | 仇祝令, 查宇飞, 朱鹏, 等. 基于孪生神经网络在线判别特征的视觉跟踪算法[J]. 光学学报, 2019, 39(9): 253-261. |
QIU Z L, ZHA Y F, ZHU P, et al. Visual tracking algorithm based on online feature discrimination with siamese net-work[J]. Acta Optica Sinica, 2019, 39(9): 253-261. | |
[10] | PU S, SONG Y B, MA C, et al. Deep attentive tracking via reciprocative learning[C]// Proceedings of the Annual Con-ference on Neural Information Processing Systems, Montréal, Dec 3-8, 2018. Red Hook: Curran Associates, 2018: 1935-1945. |
[11] | SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient based localization[C]// Proceedings of the 2017 IEEE Inter-national Conference on Computer Vision, Venice, Oct 22-29, 2017. Washington: IEEE Computer Society, 2017: 618-626. |
[12] | ZHOU B L, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative location[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Jun 27-30, 2016. Washington: IEEE Computer Society, 2016: 2921-2929. |
[13] | 贾永超, 何小卫, 郑忠龙. 融合重检测机制的卷积回归网络目标跟踪算法[J]. 计算机应用, 2019, 39(8): 2247-2251. |
JIA Y C, HE X W, ZHENG Z L. Object tracking algorithm combining re-detection mechanism and convolutional reg-ression network[J]. Journal of Computer Applications, 2019, 39(8): 2247-2251. | |
[14] |
CHEN K, TAO W B. Convolutional regression for visual tracking[J]. IEEE Transactions on Image Processing, 2018, 27(7): 3611-3620.
DOI URL |
[15] |
RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252.
DOI URL |
[16] | SIMONYAN K, ZISSERMAN A. Very deep convolutional net-works for large-scale image recognition[J]. arXiv:1409.1556, 2014. |
[17] |
WU Y, LIM J, YANG M. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848.
DOI URL |
[18] | DANELLJAN M, HÄGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision Workshops, Santiago, Dec 7-13, 2015. Washington: IEEE Computer Society, 2015: 4310-4318. |
[19] | DANELLJAN M, HÄGER G, KHAN F S, et al. Convolu-tional features for correlation filter based visual tracking[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision Workshops, Santiago, Dec 11-18, 2015. Washington: IEEE Computer Society, 2015: 621-629. |
[20] | BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple: complementary learners for real-time tracking[C]// Proceedings of the 2016 IEEE Conference on Computer Vi-sion and Pattern Recognition, Las Vegas, June 27-30, 2016. Washington: IEEE Computer Society, 2016: 1401-1409. |
[21] |
DANELLJAN M, HAGER G, KHAN F S, et al. Discrimin-ative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1561-1575.
DOI URL |
[22] | VALMADRE J, BERTINETTO L, HENRIQUES J F, et al. End to end representation learning for correlation filter based tracking[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 5000-5008. |
[23] | ZHU Z, WANG Q, LI B, et al. Distractor-aware siamese net-works for visual object tracking[C]// LNCS 11213: Proceed-ings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Cham: Springer, 2018: 103-119. |
[24] | ZHANG Z P, PENG H W. Deeper and wider siamese net-works for real-time visual tracking[C]// Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, Jun 16-20, 2019. Piscataway: IEEE, 2019: 4591-4600. |
[25] | LI P X, CHEN B Y, OUYANG W L, et al. GradNet: gradient guided network for visual object tracking[C]// Proceedings of the 2019 IEEE/CVF International Conference on Com-puter Vision, Seoul, Oct 27-Nov 2, 2019. Piscataway: IEEE, 2019: 6162-6170. |
[26] | KRISTAN M, LEONARDIS A, MATAS J, et al. The sixth visual object tracking VOT2018 challenge results[C]// LNCS 11129: Proceedings of the 15th European Conference on Computer Vision, Munich, Sep 8-14, 2018. Cham: Springer, 2018: 3-53. |
[27] | ZHU Z, HUANG G, ZOU W, et al. UCT: learning unified convolutional networks for real-time visual tracking[C]// Pro-ceedings of the 2017 IEEE International Conference on Com-puter Vision, Venice, Oct 22-29, 2017. Washington: IEEE Computer Society, 2017: 1973-1982. |
[28] | ZHANG L C, GONZALEZ-GARCIA A, VAN DE WEIJER J, et al. Learning the model update for siamese trackers[C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Oct 27-Nov 2, 2019. Piscataway: IEEE, 2019: 4010-4018. |
[29] | DANELLJAN M, BHAT G, KHAN F S, et al. ECO: efficient convolution operators for tracking[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 6931-6939. |
[30] | HE A F, LUO C, TIAN X M, et al. A twofold siamese net-work for real-time object tracking[C]// Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Washington: IEEE Computer Society, 2018: 4834-4843. |
[31] | GAO J, HU W M, LU Y, et al. Recursive least squares estimator-aided online learning for visual tracking[C]// Pro-ceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, Jun 13-19, 2020. Piscataway: IEEE, 2020: 7386-7395. |
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