A Swarm-Based Volition/Attention Framework for Object Recognition

Abstract

Visual attention helps identify the salient parts of a scene and enables efficient object recognition by allocating visual resources to more relevant regions of the scene. In this paper, we present an object recognition framework that combines top-down volitional recognition with attention processes using a swarm of cooperating intelligent agents. Each agent in the swarm is a selfcontained independent classifier that can, given any location in the image, predict the presence of a particular object of interest. Our framework combines bottom-up attention and top-down object classification using Particle Swarm Optimization (PSO) dynamics in a novel architecture that utilizes spatially-modulated evolutionary search to rapidly detect objects of interest in a scene. We use bottom-up maps that are automatically built from saliency, past swarm experience, and constraints on possible object positions to modify the swarm's behavior and help guide the swarm in locating objects. We present fast object detection/recognition results for a variety of video sequences. Our results show that our framework allows objects to be quickly and accurately located and classified using very sparse sampling of the scene.