Multi-target tracking using distributed SVM training over wireless sensor networks

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.