Abstract: This paper presents a particle swarm optimization
(PSO) based approach for multiple object tracking based on histogram
matching. To start with, gray-level histograms are calculated to
establish a feature model for each of the target object. The difference
between the gray-level histogram corresponding to each particle in the
search space and the target object is used as the fitness value. Multiple
swarms are created depending on the number of the target objects
under tracking. Because of the efficiency and simplicity of the PSO
algorithm for global optimization, target objects can be tracked as
iterations continue. Experimental results confirm that the proposed
PSO algorithm can rapidly converge, allowing real-time tracking of
each target object. When the objects being tracked move outside the
tracking range, global search capability of the PSO resumes to re-trace
the target objects.