Multi-video Supervisory Target Tracking Improved by Interactive On-line Learning

It is difficult for a human operator to monitor multiple video streams in real-time surveillance tasks. A variety of machine learning algorithms have been proposed for target tracking, but human intervention is still necessary because the algorithms have limitations to detect and recover from tracking failures. In this paper, we propose a supervisory target tracking method for multi-video surveillance tasks based on hTLD (human-in-the-loop Tracking-Learning-Detection). The human operator can monitor both the original video streams and the calculated confidence values of the tracking results. Human intervention allows to update the training data for TLD whenever a tracking failure is observed, by manually drawing a bounding box or using an eye-tracker to relocate the target. Meanwhile, an interactive on-line learning algorithm is used to learn the relations between the confidence values and the tracking results. In this way, the confidence values help avoid unnecessary intervention caused by false alarms of tracking failures. Experimental results have demonstrated that the proposed method can reduce the task completion time.

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Author and article information

Contributors

Chang Wang

Yifeng Niu

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-5

Affiliations

[0001]College of Intelligent Systems, National University of Defense Technology

De Ya Lu 109, Changsha, Hu’nan, China, 410073

Article

DOI: 10.14236/ewic/HCI2018.133

SO-VID: 76057612-ffc6-4ab8-a33f-fd882b2e520c

Copyright statement: © Wang et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

Product

Product Information: 1477-9358BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.133

Self URI (journal page): https://ewic.bcs.org/

References

W. WangB. TianY. LiuL. LiuJ. Li 2017 Study on the electrical devices detection in UAV images based on region based convolutional neural networks Journal of Geo-information Science 19 2 256 263
Y. WeiJ. QuanY. Hou 2017 Aerial Image Location of Unmanned Aerial Vehicle Based on YOLO v2 Laser&Optoelectronics Progress 11 95 104
L. ChenZ. JiangJ. YangY. Ma 2013 A coarse-to-fine approach for vehicles detection from aerial images International Conference on Computer Vision in Remote Sensing 21 221 225
H. ChengC. WengY. Chen 2012 Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks IEEE Transactions on Image Processing 21 4 2152 2159
R. T. CollinsA. J. LiptonT. KanadeH. FujiyoshiD. DugginsY. Tsin 2000 A system for video surveillance and monitoring 59 5 329 337
L. UnzuetaM. NietoA. CortesJ. BarandiaranO. OtaeguiP. Sanchez 2012 Adaptive multicue background subtraction for robust vehicle counting and classification IEEE Transactions on Intelligent Transportation Systems 13 2 527 540
S. H. KhatoonabadiI. V. Bajic 2013 Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields IEEE Press
Y. ZhuC. WangY. NiuL. Wu 2018 hTLD? A Human-in-the-loop Target Detection and Tracking Method for UAV IEEE/CSAA Guidance, Navigation and Control Conference To appear
L. JianD. YinL. ShenY. NiuL. Zhu 2017 Human machine collaborative support scheduling system of intelligence information from multiple unmanned aerial vehicles based on eye tracker Journal of Shanghai Jiaotong University 22.3 322 328
B. D. LucasT. Kanade 1981 An iterative image registration technique with an application to stereo vision International Joint Conference on Artificial Intelligence 2 674 679
Z. KalalK. MikolajczykJ. Matas 2012 Tracking-learning-detection IEEE transactions on pattern analysis and machine intelligence 34 7 1409 1422
Z. KalalJ MatasK Mikolajczyk 2010 PN learning: Bootstrapping binary classifiers by structural constraints Computer Vision and Pattern Recognition 49 56 238 49 56
J. Hartigan 1979 A K-Means Clustering Algorithm Appl Stat 28 1 100 108

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Comment on this article

[1] W. WangB. TianY. LiuL. LiuJ. Li 2017 Study on the electrical devices detection in UAV images based on region based convolutional neural networks Journal of Geo-information Science 19 2 256 263

[2] Y. WeiJ. QuanY. Hou 2017 Aerial Image Location of Unmanned Aerial Vehicle Based on YOLO v2 Laser&Optoelectronics Progress 11 95 104

[3] L. ChenZ. JiangJ. YangY. Ma 2013 A coarse-to-fine approach for vehicles detection from aerial images International Conference on Computer Vision in Remote Sensing 21 221 225

[4] H. ChengC. WengY. Chen 2012 Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks IEEE Transactions on Image Processing 21 4 2152 2159

[5] R. T. CollinsA. J. LiptonT. KanadeH. FujiyoshiD. DugginsY. Tsin 2000 A system for video surveillance and monitoring 59 5 329 337

[6] L. UnzuetaM. NietoA. CortesJ. BarandiaranO. OtaeguiP. Sanchez 2012 Adaptive multicue background subtraction for robust vehicle counting and classification IEEE Transactions on Intelligent Transportation Systems 13 2 527 540

[7] S. H. KhatoonabadiI. V. Bajic 2013 Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields IEEE Press

[8] Y. ZhuC. WangY. NiuL. Wu 2018 hTLD? A Human-in-the-loop Target Detection and Tracking Method for UAV IEEE/CSAA Guidance, Navigation and Control Conference To appear

[9] L. JianD. YinL. ShenY. NiuL. Zhu 2017 Human machine collaborative support scheduling system of intelligence information from multiple unmanned aerial vehicles based on eye tracker Journal of Shanghai Jiaotong University 22.3 322 328

[10] B. D. LucasT. Kanade 1981 An iterative image registration technique with an application to stereo vision International Joint Conference on Artificial Intelligence 2 674 679

[11] Z. KalalK. MikolajczykJ. Matas 2012 Tracking-learning-detection IEEE transactions on pattern analysis and machine intelligence 34 7 1409 1422

[12] Z. KalalJ MatasK Mikolajczyk 2010 PN learning: Bootstrapping binary classifiers by structural constraints Computer Vision and Pattern Recognition 49 56 238 49 56

[13] J. Hartigan 1979 A K-Means Clustering Algorithm Appl Stat 28 1 100 108