Bridging the gaps between cameras

TLDR

The study develops an unsupervised method to learn an activity model for a multi‑camera surveillance network from extensive observation data. The algorithm links camera views without correspondence by exploiting statistical consistency, enabling automatic topography inference and cross‑blind‑area target tracking. The approach is theoretically justified and experimentally validated.

Abstract

The paper investigates the unsupervised learning of a model of activity for a multi-camera surveillance network that can be created from a large set of observations. This enables the learning algorithm to establish links between camera views associated with an activity. The learning algorithm operates in a correspondence-free manner, exploiting the statistical consistency of the observation data. The derived model is used to automatically determine the topography of a network of cameras and to provide a means for tracking targets across the "blind" areas of the network. A theoretical justification and experimental validation of the methods are provided.