Title: Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views,
Abstract: In this study, a multiple hypothesis tracking (MHT) algorithm for
multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our
method forms track-hypothesis trees, and each branch of them represents a
multi-camera track of a target that may move within a camera as well as move
across cameras. Furthermore, multi-target tracking within a camera is performed
simultaneously with the tree formation by manipulating a status of each track
hypothesis. Each status represents three different stages of a multi-camera
track: tracking, searching, and end-of-track. The tracking status means targets
are tracked by a single camera tracker. In the searching status, the
disappeared targets are examined if they reappear in other cameras. The
end-of-track status does the target exited the camera network due to its
lengthy invisibility. These three status assists MHT to form the
track-hypothesis trees for multi-camera tracking. Furthermore, they present a
gating technique for eliminating of unlikely observation-to-track association.
In the experiments, they evaluate the proposed method using two datasets,
DukeMTMC and NLPR-MCT, which demonstrates that the proposed method outperforms
the state-of-the-art method in terms of improvement of the accuracy. In
addition, they show that the proposed method can operate in real-time and
online.