Cognitive swarms for rapid detection of objects and associations in visual imagery

摘要

We have developed a new optimization-based framework for computer vision that combines ideas from particle swarm optimization (PSO) and statistical pattern recognition to rapidly and accurately detect and classify objects in visual imagery. Swarm intelligence is used to locate objects by optimizing the classification confidence level. We have used our cognitive swarm framework to rapidly detect people, ground vehicles, and boats, and to recognize behaviors based on object associations, such as people exiting and entering vehicles, for applications in security, surveillance, target recognition, and automotive active safety.