Multiple human tracking using PHD filter in distributed camera network

The Gaussian mixture probability hypothesis density (GM-PHD) filter is a closed form approximation of the multi-target Bayes filter which can overcome most multitarget tracking problems. Limited field of view, decreasing cost of cameras, and advances of using multi-camera induce us to use large-scale camera networks. In this paper, a multihuman tracking framework using the PHD filter in a distributed camera network is proposed. Each camera tracks objects locally with PHD filter and a track-after-detect scheme and its estimates of targets are sent to neighboring nodes. Then each camera fuses its local estimates with it´s neighbors. The proposed method is evaluated on the public PETS2009 dataset. The results measured in Correct Tracking Percentage (CTP) showed a better performance compared to one of the most recent related works on the evaluated dataset.