The Probability Hypothesis Density (PHD) filter was developed as a suboptimal method for tracking a time varying number of targets. The first order statistical moment of the multiple target posterior distribution, called the Probability Hypothesis Density, gives the expected locations of the targets. This property is used instead of the full multi-target posterior distribution as it requires significantly less computation. Particle filter implementations have demonstrated the potential of the algorithm for real-time tracking applications. One of the main criticisms of the PHD filter is that there is no means of associating the same target between frames. Whilst this may be of advantage if the main concern is where the targets are, it is a major drawback if it is necessary to identify the trajectories of the different targets. Novel techniques for solving the problem of track continuity are presented here and demonstrated on simulated data. © 2005 IEEE.
|Title of host publication||Proceedings of the 2005 Intelligent Sensors, Sensor Networks and Information Processing Conference|
|Number of pages||6|
|Publication status||Published - 2005|
|Event||2005 Intelligent Sensors, Sensor Networks and Information Processing Conference - Melbourne, Australia|
Duration: 5 Dec 2005 → 8 Dec 2005
|Conference||2005 Intelligent Sensors, Sensor Networks and Information Processing Conference|
|Period||5/12/05 → 8/12/05|