Multiple Hypothesis Tracking in camera networks

In this paper we address the problem of tracking multiple targets across a network of cameras with non-overlapping fields of view. Existing methods to measure similarity between detected targets and the ones previously encountered in the network (the re-identification problem) frequently produce incorrect correspondences between observations and existing targets. We show that these issues can be corrected by Multiple Hypothesis Tracking (MHT), using its capability of disambiguation when new information is available. MHT is recognized in the multi-target tracking field by its ability to solve difficult assignment problems. Experiments both in simulation and in real world present clear advantages when using MHT with respect to the simpler MAP approach.