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.