Classifier Swarms for Human Detection in Infrared Imagery

In this paper, we describe a new method for visual recognition of objects in an image that combines feature-based object classification with efficient search mechanisms based on swarm intelligence. Our approach utilizes the particle swarm optimization algorithm (PSO), a population based evolutionary algorithm, which is effective for optimization of a wide range of functions. PSO searches a multi-dimensional solution space for a global optimum using a population of "particles" in which each particle has its own velocity vector. In our approach, we extend PSO using sequential niching methods to handle multiple minima. Also, in our approach, each particle in the swarm is actually a self-contained classifier that "flys" through the solution space seeking the most "object-like" regions. By performing this optimization, the classifier swarm simultaneously finds objects in the scene, determines their size, and optimizes the classifier parameters.