Camera Tracking Systems and their Democratization

Computer vision encompasses the analysis, processing, and interpretation of visual data. Tracking is a subset of this field, where systems recognize objects or salient features in a scene to determine their displacement across subsequent frames in a video stream. This facilitates automation, increases efficiency, and expands the functionality of these systems to applications in surveillance, medicine, and entertainment, among other fields. In recent years, Virtual Reality (VR) and Augmented Reality (AR) systems have gained popularity, prompting the development of camera tracking techniques. Camera tracking assesses the geometry and poses of a camera within a scene. Many tools are available to analyze and process camera tracking information, but most are proprietary, making information about them scarce; their availability to the general public also varies. To determine the democratization of the technology, three different tracking systems were compared. Two of these systems are standard tools used in the industry; the third system was a tracker built using OpenCV's open-source tools. A dataset of tracking values under different video parameters was gathered for all three trackers. By comparing and examining these results, it was determined that the tracking system was built using OpenCV and met industry standards. The impact of noise and lower resolution on the tracking system's performance was also assessed qualitatively by comparing tracking results in Unreal Engine. These results revealed that the democratization of tracking technology is limited by the equipment that the general public can access. This research aimed to understand better the workflow, optimization, and democratization of camera tracking systems.

Print ISSN

1545-0279

Electronic ISSN

2160-2492

Published

2026-01

Content type

Original Research

Keywords

camara tracking, open source tools, unreal engine, optic flow

DOI

10.5594/JMI.2026/YGPF6760