Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 184-190.doi: 10.11896/jsjkx.210600004

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-detector Fusion-based Depth Correlation Filtering Video Multi-target Tracking Algorithm

SHEN Xiang-pei1, DING Yan-rui1,2   

  1. 1 Laboratory of Media Design and Software Technology,Jiangnan University,Wuxi,Jiangsu 214122,China
    2 Schoolof Science,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2021-06-01 Revised:2021-10-21 Published:2022-08-02
  • About author:SHEN Xiang-pei,born in 1994,postgraduate.His main research interests include human action recognition and target tracking,etc.
    DING Yan-rui,born in 1976,Ph.D,professor.Her main research interests include intelligent computing and artificial intelligence,etc.
  • Supported by:
    National Natural Science Foundation of China(61772237) and Six Talent Climax Foundation of Jiangsu(XYDXX-030).

PDF (PC)

441

Abstract: In the detection and tracking task,the detector has mis-detected and missed targets.For video multi-target tracking algorithms that rely on detection information,there will be a large number of false tracking targets and missed targets.Such missed and false targets will last for dozens of frames,resulting in reduced tracking accuracy.Due to this reason,a multi-detector fusion deep correlation filter video multi-target tracking algorithm is proposed.It uses the information of multiple detectors and proposes a new fusion mechanism to reduce the number of missed detections and false detections caused by a single detector,and break the performance limitations of a single detector,which makes the acquisition of new targets more reliable.On the other hand,the deep correlation filter algorithm ECO is used to track the targets one by one,and a series of improvements are proposed on the basis of the original algorithm ECO,which is more suitable for the video multi-target tracking task,and reduces the number of missed targets and identity tag jumps.Finally,experiments are carried out on the MOT17 data set,compared with the traditional video multi-target tracking method IOU17,MOTA of the proposed algorithm improves from 47.6 to 50.3.It is proved that this method has made great improvement in the research of multi-target tracking.

Key words: Deep correlation filter, Detection and tracking, Multi-detector fusion, Multi-target tracking

CLC Number: 

  • TP391
[1]FU Z,FENG P,ANGELINI F,et al.Particle PHD Filter Based Multiple Human Tracking Using Online Group-Structured Dictionary Learning [J].IEEE Access,2018,14764-14778.
[2]KUTSCHBACH T,BOCHINSKI E,EISELEIN V,et al.Se-quential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tra-cking in Video Data[C]//International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017.IEEE,2017.
[3]FU Z,NAQVI S M,CHAMBERS J A.Collaborative Detector Fusion of Data-Driven PHD Filter for Online Multiple Human Tracking[C]//2018 International Conference on Information Fusion.2018.
[4]DU Y K,JEON M.Robust multi-Bernoulli filtering for visualtracking[C]//The 2014 International Conference on Control,Automation and Information Sciences(ICCAIS 2014).IEEE,2015.
[5]DU Y K.Multi-Bernoulli filtering for keypoint-based visualtracking[C]//2016 International Conference on Control,Automation and Information Sciences(ICCAIS).IEEE,2017.
[6]FU Z,ANGELINI F,NAQVI S M,et al.GM-PHD Filter Based Online Multiple Human Tracking Using Deep Discriminative Correlation Matching[C]//2018 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2018.
[7]LU Z,MAATEN L.Structure Preserving Object Tracking[C]//IEEE Conference on Computer Vision & Pattern Recognition.IEEE,2013.
[8]ZHANG L,MAATEN L.Preserving Structure in Model-FreeTracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(4):756-769.
[9]CHU P,LING H.Famnet:Joint learning of feature,affinity and multi-dimensional assignment for online multiple object tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:6172-6181.
[10]HENSCHEL R,LEAL-TAIXE L,CREMERS D,et al.Fusion of head and full-body detectors for multi-object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2018:1428-1437.
[11]HU W,TAN T,WANG L,et al.A survey on visual surveillance of object motion and behaviors[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C(Applications and Reviews),2004,34(3):334-352.
[12]LI J L,YIN K,CHU C X,et al.Overview of Video Target Tracking Technology[J].Journal of Yanshan University,2019,43(3):251-262.
[13]HENRIQUES J F,CASEIRO R,MARTINS P,et al.High-Speed Tracking with Kernelized Correlation Filters [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):583-96.
[14]DANELLJAN M,ROBINSON A,KHAN F S,et al.Beyond correlation filters:Learning continuous convolution operators for visual tracking[C]//European Conference on Computer Vision.Cham:Springer,2016:472-488.
[15]FELZENSZWALB P F,MCALLESTER D A,RAMANAN D.A discriminatively trained,multiscale,deformable part model[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2008:1-8.
[16]SZEGEDY C,TOSHEV A,ERHAN D.Deep Neural Networks for Object Detection[C]//NIPS.2013.
[17]UIJLINGS,RR J,SANDE V D,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[18]GIRSHICK R J C S.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448.
[19]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks [J].arXiv:1506.01497,2015.
[20]TAN M,PANG R,LE Q V.EfficientDet:Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10781-10790.
[21]CHEN X,FANG H,LIN T Y,et al.Microsoft coco captions:Data collection and evaluation server[J].arXiv:1504.00325,2015.
[22]BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2010:2544-2550.
[23]DANELLJAN M,HÄGER G,KHAN F S,et al.Accurate scale estimation for robust visual tracking[C]//British Machine Vision Conference,Nottingham 2014.BMVA Press,2014:1-5.
[24]YANG L,ZHU J.A scale adaptive kernel correlation filtertracker with feature integration[C]//European Conference on Computer Vision.Cham:Springer,2014:254-265.
[25]DANELLJAN M,BHAT G,KHAN F S,et al.Eco:Efficient convolution operators for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:6638-6646.
[26]MILAN A,LEAL-TAIXE L,REID I,et al.MOT16:A benchmark for multi-object tracking[J].arXiv:1603.00831,2016.
[27]DENDORFER P,OEP A,MILAN A,et al.A benchmark for single-camera multiple target tracking[J].International Journal of Computer Vision,2021,129(4):845-881.
[28]BOCHINSKI E,EISELEIN V,SIKORA T.High-speed tra-cking-by-detection without using image information[C]//2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance(AVSS).IEEE,2017:1-6.
[29]BEWLEY A,GE Z,OTT L,et al.Simple online and realtime tracking[C]//2016 IEEE International Conference on Image Processing(ICIP).IEEE,2016:3464-3468.
[30]WOJKE N,BEWLEY A,PAULUS D J I.Simple Online andRealtime Tracking with a Deep Association Metric [J].IEEE,2017:3645-3649.
[1] GONG Xuan, LE Zi-chun, WNAG Hui, WU Yu-kun. Survey of Data Association Technology in Multi-target Tracking [J]. Computer Science, 2020, 47(10): 136-144.
[2] HU Hai-gen, ZHOU Li-li, ZHOU Qian-wei, CHEN Sheng-yong, ZHANG Jun-kang. Multi-target Tracking of Cancer Cells under Phase Contrast Microscopic Images Based
on Convolutional Neural Network [J]. Computer Science, 2019, 46(5): 279-285.
[3] CHEN Jin-guang, QIN Xiao-shan and MA Li-li. Fast GM-PHD Filter for Multi-target Tracking [J]. Computer Science, 2016, 43(3): 317-321.
[4] . Clustering Data Association Algorithm to Support Multi-target Tracl}ing in WSN [J]. Computer Science, 2012, 39(Z6): 24-27.
[5] ZHU Xiao-gang,YANG Bing,XU Hua-jie. Nearest Neighbor Method Data Association Algorithm to Support Multi-target Tracking in WSN [J]. Computer Science, 2011, 38(5): 67-70.
[6] ZHAO Hua,DENG Pan,ZHANG Jian-wei. Story Link Detection Research Based on the Dynamic Extraction of Correlative Word [J]. Computer Science, 2010, 37(6): 237-239270.
[7] ZHANG Xiao-yan,WANG Ting. Research on the Dynamic Extending of Story in Story Link Detection [J]. Computer Science, 2009, 36(11): 200-203.
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.