Particle swarms as video sequence inhabitants for object tracking in computer vision

Abstract

A solution based on a swarm intelligence metaphor with a prey-predator scheme is proposed for real time object tracking in video sequences, which is a basic process in multiple computer vision tasks. Swarm predator particles fly on a Boid-like fashion over image prey pixels, using combined image features to guide individual movement rules. Object tracking emerges from interaction between predator particles and their environment. The paper includes method's description and experimental evaluations on video streams that illustrate the efficiency of swarm based methods in different vision tasks.