Abstract
High-speed object tracking is still a great challenge for video processing. Traditional cameras can hardly capture the motion trajectory of the high-speed moving object. With differential logarithmic photodetector and nanosecond response latency to fast stimuli, dynamic vision sensor (DVS) is extremely sensitive to the moving object (especially for the object with high speed). However, existing object tracking algorithms, which are limited by their frame-by-frame processing mode, are no longer suitable for DVS. In this work, we introduce a novel event coherence detection algorithm for high-speed objective tracking. The moving target is determined by judging the coherence of the event according to the event distribution at a certain moment. Experimental results demonstrate that the proposed algorithm can accurately track the small objects with high speed. Meanwhile, the proposed algorithm performs efficiently, which can run in real time.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
T. Delbruck, “Frame-free dynamic digital vision,” in Proc. Int. Symp. Secure-Life Electron., Adv. Electron. Quality Life Soc., 2008, pp. 21–26.
T. Delbruck, B. Linares-Barranco, E. Culurciello, and C. Posch, “Activitydriven, event-based vision sensors,” in Proc. IEEE Int. Symp. Circuits Syst., May 30–Jun. 2, 2010, pp. 2426–2429.
P. Lichtsteiner, C. Posch, and T. Delbruck, “A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor,” IEEE J. SolidState Circuits, vol. 43, no. 2, pp. 566–576, Feb. 2008.
Z. Ni, C. Pacoret, R. Benosman, S. Ieng, and S. Regnier, “Asynchronous ´ event based high speed vision for micro-particles tracking,” J. Microscopy, vol. 245, pp. 236–244, 2012.
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Highspeed tracking with kernelized correlation filters. TPAMI, 37(3): 583–596, 2015. 1, 6, 8.
M. Danelljan, G. H¨ager, F. Shahbaz Khan, and M. Felsberg. Accurate scale estimation for robust visual tracking. In BMVC, 2014. 1, 8.
M. Danelljan, A. Robinson, F. Shahbaz Khan, and M. Felsberg. Beyond correlation filters: Learning continuous convolution operators for visual tracking. In ECCV, 2016. 1, 2, 3, 4, 7, 8, 10, 11.
H. Nam, M. Baek, and B. Han. Modeling and propagating cnns in a tree structure for visual tracking. CoRR, abs/1608.07242, 2016. 7, 8.
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr. Fully-convolutional siamese networks for object tracking. In ECCV workshop, 2016. 2.
M. Danelljan, G. H¨ager, F. Shahbaz Khan, and M. Felsberg. Convolutional features for correlation filter based visual tracking. In ICCV Workshop, 2015. 1, 8.
A. Bewley, G. Zongyuan, F. Ramos, and B. Upcroft “Simple online and realtime tracking,” in ICIP, 2016, pp. 3464–3468.
N. Wojke, A. Bewley, D. Paulus, Simple online and realtime tracking with a deep association metric, CoRR, abs/1703.07402, 2017.
A. Milan, S. H. Rezatofighi, A. Dick, I. Reid, and K. Schindler. Online multi-target tracking using recurrent neural networks. In AAAI, 2016. 1, 2, 8.
Valentina Vasco, Arren Glover, and Chiara Bartolozzi. Fast event-based Harris corner detection exploiting the advantages of event-driven cameras. In IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS), 2016. https://doi.org/10.1109/iros.2016.7759610.
Arren Glover, Chiara Bartolozzi. Robudt visual tracking eith a freely-moving event camera. In IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS), 2017.
Mitrokhin, A., Fermuller, C., Parameshwara, C., Aloimonos, Y.: Event-based Moving Object Detection and Tracking. arXiv preprint arXiv:1803.04523 (2018).
Kueng, B., Mueggler, E., Gallego, G., Scaramuzza, D.: Low-latency visual odometry using event-based feature tracks. In: IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS), Daejeon, Korea (October 2016) 16–23.
Zhu, A.Z., Atanasov, N., Daniilidis, K.: Event-based feature tracking with probabilistic data association. In: IEEE Int. Conf. Robot. Autom. (ICRA). (2017) 4465–4470.
H. Liu, D. P. Moeys, G. Das, D. Neil, S.-C. Liu, and T. Delbruck, “Combined frame- and event-based detection and tracking,” in Int. Conf. on Circuits and Systems (ISCAS), 2016.
V. Vasco, A. Glover, E. Mueggler, D. Scaramuzza, L. Natale, and C. Bartolozzi, “Independent motion detection with event-driven cameras,” in Advanced Robotics (ICAR), 2017 18th International Conference on. IEEE, 2017, pp. 530–536.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, J., Zhang, K., Zhang, Y., Xie, X., Shi, G. (2019). High-Speed Object Tracking with Dynamic Vision Sensor. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 5th China High Resolution Earth Observation Conference (CHREOC 2018). CHREOC 2018. Lecture Notes in Electrical Engineering, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-13-6553-9_18
Download citation
DOI: https://doi.org/10.1007/978-981-13-6553-9_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6552-2
Online ISBN: 978-981-13-6553-9
eBook Packages: EngineeringEngineering (R0)