^⁸The improvements of optical observation systems in the last decades have enabled them to be installed around the globe to observe and record meteor phenomena for astronomy. In this process an automated meteor detection algorithm is essential to handle the influx of meteor data, to then be used for scientific purposes. This thesis presents implementation, modification and a test result of the Raspberry Pi Meteor Station (RMS) meteor detection algorithm implemented into Institutet för rymdfysik (IRF)'s AllSky7+ camera system software. AllSky7+ is an optical system composed of eight cameras that together can record close to the full hemisphere. The algorithm uses four images constructed from a one-minute video, captured by the system. These images are used to filter out unwanted features by a set of processes and together with a Hough transform algorithm extract data points corresponding to the meteor event. The data points can then be astrometrically calibrated for use in multi-station analysis of the meteor phenomena and meteoroid's trajectory calculations. The thesis presents initial evaluation test of AIDA-Tools, an astrometric calibration software developed by IRF, with the intention of its implementation into the software. The result of the initial test of the detection algorithm achieved an accuracy of 95.2% for true positive events. When processing a smaller dataset with known meteors, it achieved a precision of 87.5% in identifying meteors. The initial tests of AIDA-Tools achieved an accuracy equivalent to other astrometric calibration tools working with similar data, but while using a simpler optical model. While some optimization of the software remains and further testing is needed for a better estimation of the software's overall performance, the initial test showed a good baseline for accurately detecting meteors and reducing manual work.