Extended Object Tracking Using Sets Of Trajectories with a PHD Filter
This work addresses a specific limitation in multiple object tracking for scenarios with extended objects, offering an incremental improvement over prior methods.
The paper tackles the problem of tracking multiple extended objects that generate multiple measurements per scan by developing a Gamma Gaussian inverse Wishish mixture PHD filter that directly estimates sets of trajectories, showing it can estimate object trajectories more reliably compared to an existing filter using labeling schemes.
PHD filtering is a common and effective multiple object tracking (MOT) algorithm used in scenarios where the number of objects and their states are unknown. In scenarios where each object can generate multiple measurements per scan, some PHD filters can estimate the extent of the objects as well as their kinematic properties. Most of these approaches are, however, not able to inherently estimate trajectories and rely on ad-hoc methods, such as different labeling schemes, to build trajectories from the state estimates. This paper presents a Gamma Gaussian inverse Wishart mixture PHD filter that can directly estimate sets of trajectories of extended targets by expanding previous research on tracking sets of trajectories for point source objects to handle extended objects. The new filter is compared to an existing extended PHD filter that uses a labeling scheme to build trajectories, and it is shown that the new filter can estimate object trajectories more reliably.