Practical In Situ Implementation of a Multicamera Multisystem Calibration

Consumer-grade cameras are generally low-cost and available off-the-shelf, so having multicamera photogrammetric systems for 3D reconstruction is both financially feasible and practical. Such systems can be deployed in many different types of applications: infrastructure health monitoring, cultural heritage documentation, bio-medicine, as-built surveys, and indoor or outdoor mobile mapping for example. A geometric system calibration is usually necessary before a data acquisition mission in order for the results to have optimal accuracy. A typical system calibration must address the estimation of both the interior and the exterior, or relative, orientation parameters for each camera in the system. This article reviews different ways of performing a calibration of a photogrammetric system consisting of multiple cameras. It then proposes a methodology for the simultaneous estimation of both the interior and the relative orientation parameters which can work in several different types of scenarios including a multicamera multisystem calibration. A rigorous in situ system calibration was successfully implemented and tested. The same algorithm is able to handle the equivalent to a traditional-style bundle adjustment, that is, a network solution without constraints, for a single or multicamera calibrations, and the proposed bundle adjustment with built-in relative orientation constraints for the calibration of a system or multiple systems of cameras

RGB-D Multicamera Object Detection and Tracking Implemented through Deep Learning

In this thesis we present the development of a multi object detection and tracking system in low light environment implemented by using a RGB-D multicamera system and the deep learning framework. For better understanding how the system works, some hardware and software components are presented such as RGB-D sensor cameras, multi object detection and tracking techniques. In addition a brief introduction of the main concepts of the neural networks are presented

Vision-Based Production of Personalized Video

In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach