Cooperative localization and tracking with a camera-based WSN

This paper presents a vision-based system for cooperative object detection, localization and tracking using Wireless Sensor Networks (WSNs). The proposed system exploits the distributed sensing capabilities, communication infrastructure and parallel computing capabilities of the WSN. To reduce the bandwidth requirements, the images captured are processed at each camera node with the objective of extracting the location of the object on each image plane, which is transmitted to the WSN. The measures from all the camera nodes are processed by means of sensor fusion techniques such as Maximum Likelihood (ML) and Extended Kalman Filter (EKF). The paper describes hardware and software aspects and presents some experimental results.

[1]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

[2]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[3]  Sung Soo Kim,et al.  Maximum Energy Routing Protocol Based on Strong Head in Wireless Sensor Networks , 2007, Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007).

[4]  David E. Culler,et al.  Analysis of wireless sensor networks for habitat monitoring , 2004 .

[5]  Gyula Simon,et al.  The flooding time synchronization protocol , 2004, SenSys '04.

[6]  T. Wark,et al.  Real-time Image Streaming over a Low-Bandwidth Wireless Camera Network , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[7]  Dan Schonfeld,et al.  Decentralized Multiple Camera Multiple Object Tracking , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[8]  J. Cano,et al.  A performance comparison of energy consumption for Mobile Ad Hoc Network routing protocols , 2000, Proceedings 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (Cat. No.PR00728).

[9]  Joan García Haro,et al.  Estimación de la capacidad efectiva de las redes IEEE 802.15.4 en interferencia de redes IEEE 802.11 , 2007 .

[10]  A. Agogino,et al.  Wireless Sensor Networks for Commercial Lighting Control : Decision Making with Multi-agent Systems , 2004 .

[11]  Kristofer S. J. Pister,et al.  RF Time of Flight Ranging for Wireless Sensor Network Localization , 2006, 2006 International Workshop on Intelligent Solutions in Embedded Systems.

[12]  Jiann-Liang Chen,et al.  Cluster based self-organization management protocols for wireless sensor networks , 2006, IEEE Transactions on Consumer Electronics.

[13]  Begoña C. Arrue,et al.  Computer vision techniques for forest fire perception , 2008, Image Vis. Comput..

[14]  Takeo Kanade,et al.  Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision , 1993, IEEE Trans. Robotics Autom..

[15]  Aníbal Ollero,et al.  A probabilistic framework for entire WSN localization using a mobile robot , 2008, Robotics Auton. Syst..

[16]  Sokwoo Rhee,et al.  The Applicability of Wireless Technologies for Industrial Manufacturing Applications Including Cement Manufacturing , 2008, 2008 IEEE Cement Industry Technical Conference Record.

[17]  P.K.K. Loh,et al.  An efficient and reliable routing protocol for wireless sensor networks , 2005, Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks.

[18]  M. Ibrahim Sezan,et al.  A robust real-time face tracking algorithm , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[19]  Vijay Kumar,et al.  Cooperative air and ground surveillance , 2006, IEEE Robotics & Automation Magazine.