Object association across PTZ cameras using logistic MIL

Abstract

We propose a novel approach to associate objects across multiple PTZ cameras that can be used to perform camera handoff in wide-area surveillance scenarios. While previous approaches relied on geometric, appearance, or correlation-based information for establishing correspondences between static cameras, they each have well-known limitations and are not extendable to wide-area settings with PTZ cameras. In our approach, the slave camera only passively follows the target (by loose registration with the master) and bootstraps itself from its own incoming imagery, thus effectively circumventing the problems faced by previous approaches and avoiding the need to perform any model transfer. Towards this goal, we also propose a novel Multiple Instance Learning (MIL) formulation for the problem based on the logistic softmax function of covariance-based region features within a MAP estimation framework. We demonstrate our approach with multiple PTZ camera sequences in typical outdoor surveillance settings and show a comparison with state-of-the-art approaches.