Managing Particle Spread via Hybrid Particle Filter/Kernel Mean Shift Tracking
Tony Pridmore, Asad Naeem, Steven J. Mills · 2007
Particle filtering provides a well-developed and widely adopted approach to visual tracking. For effective tracking in real-world environments the particle set must sample widely enough that it can represent alternative target states in areas of ambiguity. It must not, however, become diffuse, spreading across the image plane rather than clustering around the object(s) of interest. A key issue in the design of particle filter-based trackers is how to manage the spread of the particle set to balance these conflicting requirements. To be computationally efficient, balance must be achieved with as small a particle set as reasonably possible. A number of hybrid particle filter/mean-shift trackers have recently been proposed. We believe that their strength lies in their ability to alternately disperse and cluster particles together, providing both a degree of balance and a reduced particle set. We present a novel hybrid of the annealed particle filter and kernel mean-shift algorithms that emphasises this behaviour. The algorithm has been applied to a wide variety of artificial and real image sequences. The method has performance and efficiency advantages over both pure kernel mean-shift and particle filtering trackers and existing hybrid algorithms