  In this project, we implement a multiple object tracker, following the
tracking-by-detection paradigm, as an extension of an existing method. It works
by modelling the movement of objects by solving the filtering problem, and
associating detections with predicted new locations in new frames using the
Hungarian algorithm. Three different similarity measures are used, which use
the location and shape of the bounding boxes. Compared to other trackers on the
MOTChallenge leaderboard, our method, referred to as C++SORT, is the fastest
non-anonymous submission, while also achieving decent score on other metrics.
By running our model on the Okutama-Action dataset, sampled at different
frame-rates, we show that the performance is greatly reduced when running the
model - including detecting objects - in real-time. In most metrics, the score
is reduced by 50%, but in certain cases as much as 90%. We argue that this
indicates that other, slower methods could not be used for tracking in
real-time, but that more research is required specifically on this.
