Multiple hypothesis tracking algorithm for multi‐target multi‐camera tracking with disjoint views

In this study, a multiple hypothesis tracking (MHT) algorithm for multi‐target multi‐camera tracking (MCT) with disjoint views is proposed. The authors' 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, a gating technique which eliminates the unlikely observation‐to‐track association using space‐time information has been introduced. In the experiments, the proposed method has been tested using two datasets, DukeMTMC and NLPR\MCT, which demonstrates that the method outperforms the state‐of‐the‐art method in terms of improvement of the accuracy. In addition, real‐time and online performance of proposed method is also showed in this study.