Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700113-8.doi: 10.11896/jsjkx.230700113

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Object Tracking of Structured SVM Based on DIoU Loss and Smoothness Constraints

SUN Ziwen1, YUAN Guanglin2, LI Congli1, QIN Xiaoyan2, ZHU Hong2   

  1. 1 Department of Weaponry Engineering of PLA Army Academy of Artillery and Air Defense,Hefei 230031,China
    2 Department of Information Engineering of PLA Army Academy of Artillery and Air Defense,Hefei 230031,China
  • Published:2024-06-06
  • About author:SUN Ziwen,born in 1996,Ph.D candidate.His main research interests include computer vision and object trac-king.
    YUAN Guanglin,born in 1976,Ph.D,associate professor.His main research interests include computer vision,object tracking and machine learning.
  • Supported by:
    Natural Science Foundation of Anhui Province,China(2008085QF325)

PDF (PC)

254

Abstract: Object tracking based on structured support vector machine has been widely concerned because of its excellent performance.However,the existing methods have the problems of imprecise loss function and model drift.To solve these two pro-blems,firstly,a structured SVM model is proposed based on DIoU loss and smoothness constraints.Secondly,DIoU function and L2 norm of the difference between wt and wt－1 are used respectively as the loss functions and the smoothness constraints in the model.Thirdly,the algorithm for the proposed model is designed with the dual coordinate descent principle.Finally,a multi-scale object tracking method is implemented via the proposed structured SVM on the basis of DIoU loss and smoothness constraints.The proposed object tracking method is experimentally validated on the OTB100 and VOT-ST2021 datasets,and the experimental results show that the tracking success rate of the Scale-DCSSVM on the OTB100 is 1.1％ higher than the DeepSRDCF,and the EAO on VOT-ST2021 is 1.2％ higher than the E.T.Track.The proposed object tracking method has superior performance.

Key words: Object tracking, Structured SVM, DIoU loss, Smoothness constraints

CLC Number: 

  • TP391.41
[1]ROSS D A,LIM J,LINR S,et al.Incremental Learning for Robust Visual Tracking[J].International Journal of Computer Vision,2008,77(1/2/3):125-141.
[2]MEI X,LING H.Robust visual tracking using L1 minimization[C]//Proceedings of IEEE Conference on Computer Vision.Kyoto,Japan:IEEE Computer Society Press,2009:1436-1443.
[3]SUN J,XIANG W,TAN S K,et al.Improved Kernel Correlation Filter Tracking Algorithm[J].Computer Engineering and Applications,2018,54(9):178-182.
[4]BABENKO B,YANG M H,BELONGIE S.Robust object tra-cking with online multiple instance learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(8):1619-1632.
[5]KALAL Z,MIKOLAJCZYK K,MATAS J.Tracking-learning-detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
[6]SAFFARI A,LEISTNER C,SANTNER J,et al.Online random forests[C]//Proceedings of the IEEE Conference on Computer Vision.Kyoto,Japan:IEEE Computer Society Press,2009:1393-1400.
[7]HARE S,SAffARI A,TORR P H S.Struck:structured output tracking with kernels[C]//Proceedings of IEEE International Conference on Computer Vision.Barcelona,Spain:IEEE Computer Society Press,2011:263-270.
[8]WANG N,YEUNG D Y.Learning a Deep Compact Image Representation for Visual Tracking[C]//Proceedings of Neural Information Processing Systems.Curran Associates:IEEE Computer Society Press,2013:809-817.
[9]WEI Y Q,YANG X J.Kernel Correlation Filtering for VisualTracking of Deep Features[J].Computer Engineering and Applications,2020,56(15):147-152.
[10]AVIDAN S.Support vector tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):1064-1072.
[11]HARE S,SAffARI A,TORR P H S.Struck:structured output tracking with kernels[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(10):2096-2109.
[12]YAO R,SHI Q,SHEN C,et al.Robust Tracking with Weighted Online Structured Learning[C]//Proceedings of the European conference on Computer Vision.Berlin Heidelberg:Springer,2012,Part III:158-172.
[13]YAO R,SHI Q,SHEN C,et al.Part-based Visual Tracking with Online Latent Structural Learning[C]//Proceedings of Computer Vision and Pattern Recognition.Portland,USA:IEEE Computer Society Press,2013:2363-2370.
[14]BAI Y,TANG M.Robust Tracking via Weakly SupervisedRanking SVM[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Providence,RI,USA:IEEE Computer Society Press,2012:1854-1861.
[15]ZHANG J,MA S,SCLAROFF S.Meem:Robust tracking viamultiple experts using entropy minimization[C]//Proceedings of European Conference on Computer Vision.Zurich,Switzerland:IEEE Computer Society Press,2014:188-203.
[16]HONG S,YOU T,KWAK S,et al.Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network[C]//Proceedings of International Conference on Machine Learning.Lille,France:IEEE Computer Society Press,2015:597-606.
[17]NING J F,YANG J M,JIANG S J,et al.Object tracking viadual linear structured SVM and explicit feature map[C]//Procee-dings of IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE Computer Society Press,2016:4266-4274.
[18]WANG M M,LIU Y,HUANG Z Y.Large Margin ObjectTracking with Circulant Feature Maps[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu,USA,2017:4800-4808.
[19]JI Z J,FENG K,QIANY H.Part-based Visual Tracking via Structural Support Correlation Filter[J].arXiv:1805.09971,2018.
[20]ZUO W,WU X,LIN L,et al.Learning Support Correlation Filters for Visual Tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,41(5):1158-1172.
[21]WANG C Y,WANG S Q,ZHANG L J,et al.A Multi featureDSST Target Tracking Algorithm Based on SVM Fusion[J].Ordnance Industry Automation,2021,40(7):39-45,66.
[22]QIAN Y N,CHEN Y W,LI G.Passive sonar target association tracking method based on historical motion feature constraints and SVM spectrum classification[J].Journal of Electronics and Information Technology,2023,45(8):2991-3001.
[23]ZHEN Z H,WANG P,LIU W,et al.Distance-IoU Loss:Faster and Better Learning for Bounding Box Regression[C]//Procee-dings of the AAAI Innovative Applications of Artificial Intelligence Conference.New York,USA,2020:12993-13000.
[24]RAMANAN D.Dual coordinate solvers for large-scale structu-ral svms[J].arXiv:1312.1743v2,2014.
[25]WU Y,LIM J,YANG M H.Object tracking benchmark[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1834-1848.
[26]ZABIH R,WOODFILL J.Non-parametric Local Transforms for Computing Visual Correspondence[C]//Proceedings of the European Conference on Computer Vision,Stockholm,Sweden.IEEE Computer Society Press,1994:151-158.
[27]KRISTAN M,MATAS J,LEONARDISA,et al.The Ninth Vi-sual Object Tracking[C]//Proceedings of the{VOT2021} Challenge Results International Conference on Computer Vision Workshops.Montreal,BC,Canada,2021:2711-2738.
[28]HONG S,YOU T,KWAK S,et al.Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network[C]//Proceedings of the International Conference on Machine Learning.Lille:IEEE Computer Society Press,2015:597-606.
[29]BERTINETTO L,VALMADRE J,HENRIQUES J F,et al.Fully-Convolutional Siamese Networks for Object Tracking[C]//Proceedings of the European Conference on Computer Vision.Amsterdam,The Netherlands:IEEE Computer Society Press,2016:850-865.
[30]SONG Y B,MA C,GONG L J,et al.Convolutional ResidualLearning for Visual Tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice,Italy:IEEE Computer Society Press,2017:2574-2583.
[31]DANELLJAN M,HAGER G,KHAN F S,et al.Convolutional Features for Correlation Filter Based Visual Tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.Santiago:IEEE Computer Society Press,2015:621-629.
[32]MA C,HUANG J B,YANG X K,et al.Hierarchical Convolutional Features for Visual Tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.Santiago,Chile:IEEE Computer Society Press,2015:3074-3082.
[33]HONG Z B,CHEN Z,WANG C H,et al.Multi-Store Tracker:ACognitive Psychology Inspired Approach to Object Tracking[C]//Proceedings of the International Conference on Computer Vision and Pattern Recognition.Boston,USA:IEEE Computer Society Press,2015:749-758.
[34]DANELLJAN M,GUSTAV H,KHAN F S,et al.LearningSpatially Regularized Correlation Filters for Visual Tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.Santiago:IEEE Computer Society Press,2015:4310-4318.
[35]BERTINETTO L,VALMADRE J,GOLODETZ S,et al.Staple:Complementary Learners for Real-Time Tracking[C]//Proceedings of the International Conference on Computer Vision and Pattern Recognition.Las Vegas,NV:IEEE Computer Society Press,2016:1401-1409.
[36]LI B,WU W,WANG Q,et al.SiamRPN++:Evolution of Siamese Visual Tracking With Very Deep Networks[C]//Procee-dings of the Conference on Computer Vision and Pattern Recognition,Long Beach,CA,USA.2019:4282-4291.
[37]LUKEZIC A,MATAS J,KRISTAN M.D3S-A discriminative single shot segmentation tracker[C]//Proceedings of the Conference on Computer Vision and Pattern Recognition.Seattle,WA,USA,2020:7131-7140.
[38]LI X,MA C,WU B Y,et al.Target-Aware Deep Tracking[C]//Proceedings of the International Conference on Computer Vision and Pattern Recognition.CA,USA,2019:1369-1378.
[39]YAN B,PENG H W,WU K,et al.Lighttrack:Finding lightweight neural networks for object tracking via one-shot architecture search[C]//Proceedings of the Conference on Computer Vision and Pattern Recognition.Virtual,2021:15180-15189.
[40]BLATTER P,KANAKIS M,DANELLJAN M,et al.Efficient Visual Tracking with Exemplar Transformers[C]//Procee-dings of the Winter Conference on Applications of Computer Vision,Waikoloa,HI,USA.2023:1571-1581.
[1] ZHENG Shenhai, GAO Xi, LIU Pengwei, LI Weisheng. Occluded Video Instance Segmentation Method Based on Feature Fusion of Tracking and Detection in Time Sequence [J]. Computer Science, 2024, 51(6A): 230600186-6.
[2] WANG Luo, LI Biao, FU Ruigang. Infrared Ground Multi-object Tracking Method Based on Improved ByteTrack Algorithm [J]. Computer Science, 2023, 50(9): 176-183.
[3] LUO Huilan, LONG Jun, LIANG Miaomiao. Attentional Feature Fusion Approach for Siamese Network Based Object Tracking [J]. Computer Science, 2023, 50(6A): 220300237-9.
[4] ZENG Zehua, LUO Huilan. Cross-dataset Learning Combining Multi-object Tracking and Human Pose Estimation [J]. Computer Science, 2023, 50(6A): 220400199-7.
[5] WU Han, NIE Jiahao, ZHANG Zhaowei, HE Zhiwei, GAO Mingyu. Deep Learning-based Visual Multiple Object Tracking:A Review [J]. Computer Science, 2023, 50(4): 77-87.
[6] WANG Chao, WANG Kai. Visual Object Tracking Based on Adaptive Search Range Adjustment [J]. Computer Science, 2023, 50(11A): 221000172-6.
[7] WANG Lin, LIU Zhe, SHI Dianxi, ZHOU Chenlei, YANG Shaowu, ZHANG Yongjun. Fusion Tracker:Single-object Tracking Framework Fusing Image Features and Event Features [J]. Computer Science, 2023, 50(10): 96-103.
[8] CHEN Yunfang, LU Yangyang, ZHOU Xin, ZHANG Wei. Multi-object Tracking Based on Cross-correlation Attention and Chained Frames [J]. Computer Science, 2023, 50(1): 131-137.
[9] LI Xuehui, ZHANG Yongjun, SHI Dianxi, XU Huachi, SHI Yanyan. AFTM:Anchor-free Object Tracking Method with Attention Features [J]. Computer Science, 2023, 50(1): 138-146.
[10] MA Bin, FU Yong-kang, WANG Chun-peng, LI Jian, WANG Yu-li. High Performance Insulators Location Scheme Based on YOLOv4 with GDIoU Loss Function [J]. Computer Science, 2022, 49(6A): 412-417.
[11] ZHAO Yue, YU Zhi-bin, LI Yong-chun. Cross-attention Guided Siamese Network Object Tracking Algorithm [J]. Computer Science, 2022, 49(3): 163-169.
[12] YANG Lan-lan, WANG Wen-qi, WANG Fu-tian. RGBT Object Tracking Based on High Rank Feature and Position Attention [J]. Computer Science, 2022, 49(12): 236-243.
[13] CHEN Yuan, HUI Yan, HU Xiu-hua. Background-aware Correlation Filter Tracking Algorithm with Adaptive Scaling and Learning Rate Adjustment [J]. Computer Science, 2021, 48(5): 177-183.
[14] CHENG Xu, CUI Yi-ping, SONG Chen, CHEN Bei-jing, ZHENG Yu-hui, SHI Jin-gang. Object Tracking Algorithm Based on Temporal-Spatial Attention Mechanism [J]. Computer Science, 2021, 48(4): 123-129.
[15] ZHANG Kai-hua, FAN Jia-qing, LIU Qing-shan. Advances on Visual Object Tracking in Past Decade [J]. Computer Science, 2021, 48(3): 40-49.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.