TVV: Real-Time Visual Identity and Tracking with Edge Computing
Authors: 
Xinpeng Zhang, Junchen Guo, Chunya Liu, Jie Zhou, Yao Luo, Long Liu, Meng Jin, Ziqiang Zhou, Zhoubin LiuAuthors Info & Claims
Abstract
Video surveillance today has become pervasive, making visual identification and tracking technology attractive to a broad class of applications like traffic counting, crime tracking, and Blockchain. However, visual tracking is also a victim of the ubiquity of surveillance camera: a huge amount of data that generated by the cameras leads to severe congestion problem, which decreases the frame rate and in turn affects the tracking accuracy. In this paper, we present TVV, a real-time visual tracking system that leverages edge computing to support accurate and continuous tracking in large scale areas. The design of TVV is based on a insight that almost 80% of frames in a video stream exhibit high quality, and such frames can be processed on the edge nodes using a lightweight filtering method named KCF. Based on this insight, TVV adaptively load the visual tacking task on the edge or the server, based on the quality of the currently generated frame. In this way, the traffic load is largely decreased, without sacrificing the tracking accuracy. Our experimental result show that the average frame rate of TVV achieves 45.75 fps, outperforming most state-of-the-art visual tracking approaches.
References
[1]
G. Ananthanarayanan, P. Bahl, P. Bodı́k, K. Chintalapudi, M. Philipose, L. Ravindranath, and S. Sinha. Real-time video analytics: The killer app for edge computing. computer, 50(10):58–67, 2017.
[2]
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr. Fully-convolutional siamese networks for object tracking. In European conference on computer vision, pages 850–865. Springer, 2016.
[3]
N. Chen, Y. Chen, Y. You, H. Ling, P. Liang, and R. Zimmermann. Dynamic urban surveillance video stream processing using fog computing. In 2016 IEEE second international conference on multimedia big data (BigMM), pages 105–112. IEEE, 2016.
[4]
J. Dick, C. Phillips, S. H. Mortazavi, and E. de Lara. High speed object tracking using edge computing. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, page 26. ACM, 2017.
[5]
H. Fan and H. Ling. Parallel tracking and verifying. 2018.
[6]
J. Guo, Y. He, and X. Zheng. Pangu: Towards a software-defined architecture for multi-function wireless sensor networks. In Parallel and Distributed Systems (ICPADS), 2017 IEEE 23rd International Conference on, pages 730–737. IEEE, 2017.
[7]
Y. He, J. Guo, and X. Zheng. From surveillance to digital twin: Challenges and recent advances of signal processing for industrial iot.
[8]
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Exploiting the circulant structure of tracking-by-detection with kernels. In European conference on computer vision, pages 702–715. Springer, 2012.
[9]
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3):583–596, 2015.
[10]
C. Jiang, Y. He, X. Zheng, and Y. Liu. Orientation-aware rfid tracking with centimeter-level accuracy. In Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, pages 290–301. IEEE Press, 2018.
[11]
N. McLaughlin, J. Martinez del Rincon, and P. Miller. Recurrent convolutional network for video-based person re-identification. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
[12]
H. Nam and B. Han. Learning multi-domain convolutional neural networks for visual tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4293–4302, 2016.
[13]
E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi. Performance measures and a data set for multi-target, multi-camera tracking. In European Conference on Computer Vision, pages 17–35, 2016.
[14]
M. Schneider, J. Rambach, and D. Stricker. Augmented reality based on edge computing using the example of remote live support. In Industrial Technology (ICIT), 2017 IEEE International Conference on, pages 1277–1282. IEEE, 2017.
[15]
D. Simonnet, M. Lewandowski, S. A. Velastin, J. Orwell, and E. Turkbeyler. Re-identification of pedestrians in crowds using dynamic time warping. In European Conference on Computer Vision, pages 423– 432. Springer, 2012.
[16]
H. Sun, X. Liang, and W. Shi. Vu: video usefulness and its application in large-scale video surveillance systems: an early experience. In Proceedings of the Workshop on Smart Internet of Things, page 6. ACM, 2017.
[17]
N. Wang, S. Li, A. Gupta, and D.-Y. Yeung. Transferring rich feature hierarchies for robust visual tracking. arXiv preprint arXiv:1501.04587, 2015.
[18]
Z. Zhou, Y. Huang, W. Wang, L. Wang, and T. Tan. See the forest for the trees: Joint spatial and temporal recurrent neural networks for video-based person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition, pages 6776–6785, 2017.
- TVV: Real-Time Visual Identity and Tracking with Edge Computing
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Information
Published In
February 2019
436 pages
ISBN:9780994988638
- General Chairs:
- Yunhao Liu,
- Guoliang Xing,
- Program Chairs:
- Yuan He,
- Gian Pietro Picco
Sponsors
- EWSN: International Conference on Embedded Wireless Systems and Networks
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Publisher
Junction Publishing
United States
Publication History
Published: 15 March 2019
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