Multiple target localization in wireless visual sensor networks

Wei LI, Wei ZHANG

PDF(575 KB)
PDF(575 KB)
Front. Comput. Sci. ›› 2013, Vol. 7 ›› Issue (4) : 496-504. DOI: 10.1007/s11704-013-2197-0
RESEARCH ARTICLE

Multiple target localization in wireless visual sensor networks

Author information +
History +

Abstract

Target localization is an important service in wireless visual sensor networks (WVSN). Although the problem of single target localization has been intensively studied, few consider the problem of multiple target localization without prior target information in WVSN. In this paper, we first investigate the architecture of WVSN where data transmission is reduced to only target positions. Since target matching is a key issue in the multiple target localization, we propose a statistical method to match corresponding targets to located targets in world coordinates. In addition, we also consider scenarios where occlusion or limited field of view (FOV) occurs. The proposed method utilizes target images to the greatest extent. Our experimental results show that the proposed method obtains a more accurate result in targets localization compared with the camera discard scheme, and saves significant amounts of energy compared with other feature matching schemes.

Keywords

target localization / statistics / wireless visual sensor networks / occlusion / limited field of view

Cite this article

Download citation ▾
Wei LI, Wei ZHANG. Multiple target localization in wireless visual sensor networks. Front Comput Sci, 2013, 7(4): 496‒504 https://doi.org/10.1007/s11704-013-2197-0

References

[1]
Charfi Y, Wakamiya N, Murata M. Challenging issues in visual sensor networks. IEEEWireless Communications, 2009, 16(2): 44-49
CrossRef Google scholar
[2]
Liu L, Zhang X, Ma H D. Optimal node selection for target localization in wireless camera sensor networks. IEEE Transactions on Vehicular Technology, 2010, 59(7): 3562-3576
CrossRef Google scholar
[3]
Massey T, Kapur R, Dabiri F, Vu L N, Sarrafzadeh M. Localization using low-resolution optical sensors. The 4th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, 2007
[4]
Mao G Q, Fidan B, Anderson B. Wireless sensor network localization techniques. Computer Networks, 2011, 51(10): 2529-2553
CrossRef Google scholar
[5]
Rajeev S, Ananda A, Mun C, Wei T. Mobile, wireless, and sensor networks: technology, applications, and future directions. JohnWiley and Sons, 2005
[6]
Chan F, Wen C Y. Adaptive AOA/TOA localization using fuzzy particle filter for mobileWSNs. 2011 IEEE the 73rd Vehicular Technology Conference, 2011
[7]
Jajamovich G H, Wang X D. Joint multi-target tracking and sensor localization in collaborative sensor networks. IEEE Transactions on Aerospace and Electronic Systems, 2005, 47(4): 2361-2375
CrossRef Google scholar
[8]
Hu J W, Xie L H, Zhang C. Energy-based multiple target localization and pursuit in mobile sensor networks. IEEE Transactions on Instrumentation and measurement, 2012, 61(1): 212-220
CrossRef Google scholar
[9]
Mantzel W, Choi H, Baraniuk R. Distributed camera network localization. Asilomar Conference on Signals, Systems and Computers, 2004
[10]
Li W, Portilla J, Moreno F, Liang G X, Riesgo T. Improving target localization accuracy of wireless visual sensor networks. In: Proceedings of the 37th IEEE Industrial Electronics Conference, 2011
[11]
Zhang Z, Deriche R, Faugeras O, Luong Q T. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 1995, 78(1): 87-119
CrossRef Google scholar
[12]
Schmid C, Mohr R. Local gray value invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(5): 530-534
CrossRef Google scholar
[13]
Devarajan D, Radke R J, Chung H. Distributed metric calibration of ad hoc camera networks. ACM Transactions on Sensor Networks, 2006, 2(3): 380-403
CrossRef Google scholar
[14]
Lowe D G. Distinctive image features from scale-invariant key points. International Journal of Computer Vision, 2004, 60(2): 91-110
CrossRef Google scholar
[15]
Sheng X, Hu Y H. Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Transactions on Signal Processing, 2005, 53(1): 44-53
CrossRef Google scholar
[16]
Farrell R, Garcia R, Lucarelli D, Terzis A, Wang I J. Target localization in camera wireless networks. Pervasive and Mobile Computing, 2009, 5(2): 165-181
CrossRef Google scholar
[17]
Medeiros H, Iwaki H, Park J. Online distributed calibration of a large network of wireless camera using dynamic clustering. In: Proceedings of the 2nd ACM/IEEE International Conference on Distributed Smart Cameras, 2008
[18]
Kurillo G, Li Z, Bajcsy R. Wide-area external multi-camera calibration using vision graphs and virtual calibration object. In: Proceedings of the 2nd ACM/IEEE International Conference on Distributed Smart Cameras, 2008
[19]
Akyildiz I F, Melodia T, Chowdury K R. A survey on wireless multimedia sensor networks. IEEE Wireless Communications, 2007, 14(6): 32-39
CrossRef Google scholar
[20]
Akyildiz I F, Melodia T, Chowdhury K R. A survey on wireless multimedia sensor networks. Computer Networks, 2007, 51(4): 921-960
CrossRef Google scholar
[21]
Ertin E, Fisher J, Potter L. Maximum mutual information principle for dynamic sensor query problems. In: Proceedins of the 2nd International Workshop on Information Processing in Sensor Networks, 2003
[22]
Ercan A, Yang D, El Gamal A, Guibas L. Optimal placement and selection of camera network nodes for target localization. Distributed Computing in Sensor Systems, 2006, 389-404
[23]
Heinzelman W. Application-specific protocol architectures for wireless networks. PhD dissertation, Massachusetts Institute of Technology, 2000

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(575 KB)

Accesses

Citations

Detail

Sections
Recommended

/