Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking

by   Nathanael L. Baisa, et al.

We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple targets having N different types where N≥2 based on Random Finite Set (RFS) theory, taking into account not only background false positives (clutter), but also confusions among detections of different target types, which are in general different in character from background clutter. Under the assumptions of Gaussianity and linearity, our framework extends the existing Gaussian mixture (GM) implementation of the standard PHD filter to create a N-type GM-PHD filter. The methodology is applied to real video sequences by integrating object detectors' information into this filter for two scenarios. In the first scenario, a tri-GM-PHD filter (N=3) is applied to real video sequences containing three types of multiple targets in the same scene, two football teams and a referee, using separate but confused detections. In the second scenario, we use a dual GM-PHD filter (N=2) for tracking pedestrians and vehicles in the same scene handling their detectors' confusions. For both cases, Munkres's variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. This approach is evaluated and compared to both raw detection and independent GM-PHD filters using the Optimal Sub-pattern Assignment (OSPA) metric and the discrimination rate. This shows the improved performance of our strategy on real video sequences.


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