Abstract
We describe a novel method for recognition and localization of objects in 3D space using multiple views. We pose the task of classifying and locating objects in 3D space as an optimization problem that combines 2D classifier scores from two separate views of the object. Our methods combine feature-based 2D object classification with efficient search mechanisms based on Particle Swarm Optimization (PSO) by implementing each particle as a local window classi- fier guided by PSO dynamics. We first formulate the localization problem in a multi-view framework where the domain of swarm classifier particles is extended from 2D image space to 3D space. In this scheme, swarm particles encapsulate the geometric structure that binds multiple views. Each particle is a self-contained classifier that flies through the solution space seeking the most objectlike region in space, thus effectively expanding the scope of an image based 2D classifier to 3D space. In a second formulation, we demonstrate how 2D swarm based search results obtained from individual 2D views can be used to perform 3D localization tasks. We outline a method to extend the framework from single object to multiple object localization and tracking scenarios.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.