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Paper WeB04.5

Kim, Woojin (Seoul National Univ.), YOO, Jae Hyun (Seoul National University), Kim, H. Jin (Seoul National University)

Multi-Target Tracking Using Distributed SVM Training Over Wireless Sensor Networks

Scheduled for presentation during the Regular Session "Networked Robots" (WeB04), Wednesday, May 16, 2012, 11:30−11:45, Meeting Room 4 (Chief Wabasha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on February 23, 2018

Keywords Sensor Networks, Distributed Robot Systems

Abstract

In this paper, we propose to use distributed support vector machine (SVM) training to solve a multi-target tracking problem in wireless sensor networks. We employ gossip-based incremental SVM to obtain the discriminant function. By gossiping the support vectors with neighboring sensor nodes, the local SVM training results can achieve the agreement of the sub-optimal discriminant planes. After training the local SVM at each node, we can calculate the posterior probability of the existence of the targets using Platt's method. By maximum a posterior (MAP), the target trajectories are estimated. In order to validate the proposed tracking framework in wireless sensor networks, we perform two different target-tracking experiments. The experimental results demonstrate that the proposed procedure provides a good estimator, and supports the feasibility of applying the distributed SVM training to the target tracking problems.

 

 

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